Acquisition Function-Guided Generation for Bayesian Optimization over Discrete Spaces

Tommaso Ancilli; Thomas Wedenig; Henry Moss; Robert Peharz

cs.LGOct 16, 2026

Acquisition Function-Guided Generation for Bayesian Optimization over Discrete Spaces

Authors: Tommaso Ancilli, Thomas Wedenig, Henry Moss, Robert Peharz

Organizations: Institute of Machine Learning and Neural Computation, TU Graz, Austria · Graz Center for Machine Learning (GraML), Austria · School of Mathematical Sciences, Lancaster University, UK

Abstract

Bayesian optimization (BO) over discrete spaces is challenging due to missing gradients and the sparsity of valid solutions. Latent-space BO sidesteps both issues by fitting a surrogate in the latent space of a generative model, which however substantially complicates the direct incorporation of domain knowledge that is naturally expressed in the original discrete space. In this work, we instead combine a pretrained generative model with a surrogate defined directly in data space, where inductive biases such as domain-specific kernels can be readily incorporated. Rather than maximizing the acquisition function over the discrete design space, our method samples candidates from the generative model under a distribution tilted by the acquisition function. To recover useful gradients for sampling, we embed this tilted distribution into a diffusion bridge that yields a smooth, posterior-averaged acquisition function. This leads to acquisition function guided generation (AFGG), which we instantiate with a categorical diffusion model and an autoregressive normalizing flow. Across a broad suite of molecular benchmarks, the diffusion model variant performs on par with leading BO methods, while the flow model variant outperforms state-of-the-art methods in most cases and remains highly competitive otherwise. Our implementation is available at https://anonymous.4open.science/r/afgg.

Figures & tables

Figure 1: Sequential setting. Optimization trajectories on six representative objectives, with 10 initial observations followed by 300 oracle evaluations, one per BO iteration. Curves show the median over 5 seeds.

	baselines					AFGG
Objective	CoBO	InvBO	LOL-BO	NFBO	COWBOYS	CDCD	SeqFlow
albu_sim	0.593 $\pm$ 0.111	0.629 $\pm$ 0.079	0.509 $\pm$ 0.065	0.593 $\pm$ 0.119	0.448 $\pm$ 0.037	0.691 $\pm$ 0.073	0.975 $\pm$ 0.038
amlo_mpo	0.523 $\pm$ 0.012	0.538 $\pm$ 0.050	0.516 $\pm$ 0.022	0.563 $\pm$ 0.095	0.472 $\pm$ 0.038	0.546 $\pm$ 0.039	0.565 $\pm$ 0.045
cele_red	0.262 $\pm$ 0.041	0.286 $\pm$ 0.068	0.243 $\pm$ 0.035	0.455 $\pm$ 0.035	0.243 $\pm$ 0.020	0.392 $\pm$ 0.024	0.836 $\pm$ 0.125
deco_hop	0.565 $\pm$ 0.008	0.597 $\pm$ 0.019	0.560 $\pm$ 0.009	0.673 $\pm$ 0.147	0.559 $\pm$ 0.008	0.599 $\pm$ 0.010	0.803 $\pm$ 0.175
drd2	0.510 $\pm$ 0.370	0.963 $\pm$ 0.037	0.626 $\pm$ 0.184	0.971 $\pm$ 0.066	0.796 $\pm$ 0.243	0.730 $\pm$ 0.226	1.000 $\pm$ 0.000

Table 1: Sequential benchmark (partial results). Final best score on all 25 PMO objectives after 300 oracle calls (10 initial points, 1 point per BO iteration), mean

\pm

sample standard deviation over 5 seeds. For readability the table shows five of the eleven baselines; Table 6 gives the full comparison. AFGG is shown in two instantiations: CDCD and SeqFlow . Every cell is judged against the opposing group. Bold : the cell’s mean exceeds every mean in the opposing group. Green, shaded : lead exceeds the cell’s own sample standard deviation; bold without shading denotes a smaller lead. Objective abbreviations are defined in Table 5 .

Figure 2: Batch setting, uncoupled generation. Optimization trajectories on the same six representative objectives, with 100 initial observations followed by 500 oracle evaluations in batches of five. Curves show the median over 5 seeds.

Appendix figures & tables16 assets

Supplementary material from the paper’s appendix.

Appendix

	CDCD	SeqFlow
noised variable	embedding $x$	flow latent $g_{\theta}^{-1}(x)$
schedule $(\alpha_{t},\sigma_{t})$	$(1,\,t)$ , VE	$\big(\rho_{t},\,\sqrt{1-\rho_{t}^{2}}\big)$ , VP Eq. ( 51 )
(i) $\nabla_{z_{t}}\log p_{t}$	learned Eq. ( 6 )	$-z_{t}$ , exact
(ii) draws from $p(\cdot\mid z_{t})$	mean-field $\tilde{p}$ Eq. ( 49 ), one network pass	exact Gaussian Eq. ( 27 ), $N$ passes of $g_{\theta}$
$\nabla_{z_{t}}\log\ell_{t}$	SNIS Eq. ( 42 ) or REINFORCE Eq. ( 50 )	SNIS Eq. ( 42 )
noise end	$p^{\star}_{T}\approx\mathcal{N}(0,T^{2}I)$	$\pi^{\star}_{T}\approx\mathcal{N}(0,I)$

Appendix

Table 2: What each backbone supplies to the guided score Eq. ( 19 ). Quantities (i) and (ii) are backbone-specific; the guidance term

\nabla_{z_{t}}\log\ell_{t}

is estimated from the draws of (ii) (Section 3.3 ).

objective	$K=0$	$K=32$	$\Delta$	$\Delta/\mathrm{sd}$
med2	$0.1637\pm 0.0193$	$0.1694\pm 0.0243$	$+0.0058$	$+0.26$
osmb	$0.7094\pm 0.0578$	$0.7055\pm 0.0611$	$-0.0039$	$-0.07$
adip	$0.4335\pm 0.0297$	$0.4483\pm 0.0029$	$+0.0148$	$+0.70$
zale	$0.3811\pm 0.0935$	$0.2842\pm 0.0612$	$-0.0969$	$-1.23$
rano	$0.7503\pm 0.0361$	$0.7088\pm 0.0570$	$-0.0416$	$-0.87$
qed	$0.8992\pm 0.0370$	$0.9106\pm 0.0387$	$+0.0114$	$+0.30$

Appendix

Table 3: The mean-field approximation Eq. ( 49 ) against rollouts of the reverse process.

K

is the number of inner denoising steps used to draw each of the

N

SNIS samples:

K=0

is the factorized draw from the per-position marginals Eq. ( 7 ), i.e. Eq. ( 49 ), and is what Algorithm 1 uses;

K>0

replaces it by a

K

-step ancestral rollout, which couples the positions through the network and resolves the joint. Final objective value after

300

BO epochs on six GuacaMol/TDC objectives, mean

\pm

sd over

3

seeds; higher is better.

\Delta

K{=}32

minus

K{=}0

, and the last column expresses it in units of the pooled seed-to-seed sd. At

K=32

— beyond the point where proposal fidelity saturates, and at

\approx 24\times

the wall clock (

8.77

against

0.36

h per arm) — no objective improves beyond the seed spread and one degrades.

Architecture
Hyperparameter	Value
Embedding dimension	$16$
Hidden dimension	$512$
Query/key/value dimension	$512$
Time embedding dimension	$16$
Layers	$8$

Appendix

Table 4: Hyperparameters of the CDCD generator.

Abbreviation	PMO objective	Abbreviation	PMO objective
albu_sim	albuterol_similarity	osim_mpo	osimertinib_mpo
amlo_mpo	amlodipine_mpo	peri_mpo	perindopril_mpo
cele_red	celecoxib_rediscovery	rano_mpo	ranolazine_mpo
deco_hop	deco_hop	logp	rdkit_logp
drd2	drd2_docking	qed	rdkit_qed
fexo_mpo	fexofenadine_mpo	sa	sa_tdc

Appendix

Table 5: Abbreviations of the 25 PMO objectives, used in all tables.

Figure 3: Sequential setting : full trajectories on all 25 PMO objectives (10 initial points, 300 oracle calls, 1 point per BO iteration).

	baselines											AFGG
Objective	CoBO	InvBO	LOL-BO	NFBO	CMA-ES	COWBOYS	DE	GA	HC	RLBO	TuRBO	CDCD	SeqFlow
albu_sim	0.593 $\pm$ 0.111	0.629 $\pm$ 0.079	0.509 $\pm$ 0.065	0.593 $\pm$ 0.119	0.500 $\pm$ 0.030	0.448 $\pm$ 0.037	0.488 $\pm$ 0.035	0.385 $\pm$ 0.074	0.454 $\pm$ 0.070	0.483 $\pm$ 0.091	0.475 $\pm$ 0.047	0.691 $\pm$ 0.073	0.975 $\pm$ 0.038
amlo_mpo	0.523 $\pm$ 0.012	0.538 $\pm$ 0.050	0.516 $\pm$ 0.022	0.563 $\pm$ 0.095	0.459 $\pm$ 0.038	0.472 $\pm$ 0.038	0.438 $\pm$ 0.143	0.419 $\pm$ 0.082	0.460 $\pm$ 0.048	0.491 $\pm$ 0.030	0.475 $\pm$ 0.024	0.546 $\pm$ 0.039	0.565 $\pm$ 0.045
cele_red	0.262 $\pm$ 0.041	0.286 $\pm$ 0.068	0.243 $\pm$ 0.035	0.455 $\pm$ 0.035	0.227 $\pm$ 0.032	0.243 $\pm$ 0.020	0.216 $\pm$ 0.036	0.210 $\pm$ 0.040	0.228 $\pm$ 0.032	0.224 $\pm$ 0.031	0.221 $\pm$ 0.033	0.392 $\pm$ 0.024	0.836 $\pm$ 0.125
deco_hop	0.565 $\pm$ 0.008	0.597 $\pm$ 0.019	0.560 $\pm$ 0.009	0.673 $\pm$ 0.147	0.563 $\pm$ 0.010	0.559 $\pm$ 0.008	0.571 $\pm$ 0.018	0.549 $\pm$ 0.015	0.553 $\pm$ 0.012	0.556 $\pm$ 0.015	0.552 $\pm$ 0.012	0.599 $\pm$ 0.010	0.803 $\pm$ 0.175
drd2	0.510 $\pm$ 0.370	0.963 $\pm$ 0.037	0.626 $\pm$ 0.184	0.971 $\pm$ 0.066	0.184 $\pm$ 0.068	0.796 $\pm$ 0.243	0.862 $\pm$ 0.156	0.154 $\pm$ 0.051	0.183 $\pm$ 0.045	0.380 $\pm$ 0.246	0.556 $\pm$ 0.337	0.730 $\pm$ 0.226	1.000 $\pm$ 0.000

Appendix

Table 6: Final best score on all 25 PMO objectives after 300 oracle calls (10 initial points, 1 point per BO iteration), mean

\pm

sample standard deviation over 5 seeds. AFGG is shown in two instantiations: CDCD and SeqFlow . Every cell is judged against the opposing group: an AFGG cell against the 11 baselines, a baseline cell against the two AFGG columns. Bold : the cell’s mean exceeds every mean in the opposing group. Green : that lead is significant, exceeding the cell’s own standard deviation; bold without green therefore means ahead but within noise.

Figure 4: Batch regime, uncoupled generation : 500 acquisitions (100 initial points, then 100 epochs of 5), all 25 objectives.

	baselines					AFGG
Objective	CoBO	InvBO	LOL-BO	NFBO	COWBOYS	CDCD	SeqFlow
albu_sim	0.692 $\pm$ 0.095	0.771 $\pm$ 0.057	0.579 $\pm$ 0.043	0.609 $\pm$ 0.073	0.551 $\pm$ 0.081	0.779 $\pm$ 0.073	0.960 $\pm$ 0.050
amlo_mpo	0.540 $\pm$ 0.021	0.569 $\pm$ 0.029	0.522 $\pm$ 0.026	0.582 $\pm$ 0.024	0.501 $\pm$ 0.021	0.565 $\pm$ 0.052	0.618 $\pm$ 0.075
cele_red	0.307 $\pm$ 0.035	0.366 $\pm$ 0.064	0.304 $\pm$ 0.036	0.452 $\pm$ 0.052	0.316 $\pm$ 0.052	0.457 $\pm$ 0.098	0.861 $\pm$ 0.135
deco_hop	0.588 $\pm$ 0.005	0.610 $\pm$ 0.007	0.586 $\pm$ 0.004	0.617 $\pm$ 0.018	0.583 $\pm$ 0.004	0.614 $\pm$ 0.019	0.878 $\pm$ 0.120
drd2	0.930 $\pm$ 0.090	0.996 $\pm$ 0.006	0.728 $\pm$ 0.247	0.994 $\pm$ 0.012	0.774 $\pm$ 0.210	0.792 $\pm$ 0.288	1.000 $\pm$ 0.000

Appendix

Table 7: Batch regime, uncoupled generation. Final best score on all 25 PMO objectives after 500 acquisitions (100 initial points, then 100 epochs of 5), mean

\pm

sample standard deviation over 5 seeds. AFGG generates each batch uncoupled here: every molecule is guided by its own acquisition value, and the batch only forms at selection. Every cell is judged against the opposing group: an AFGG cell against the five baselines, a baseline cell against the two AFGG columns. Bold : the cell’s mean exceeds every mean in the opposing group. Green : that lead is significant, exceeding the cell’s own standard deviation; bold without green therefore means ahead but within noise.

Figure 5: Batch regime, coupled generation : 500 acquisitions (100 initial points, then 100 epochs of 5), all 25 objectives.

	baselines					AFGG
Objective	CoBO	InvBO	LOL-BO	NFBO	COWBOYS	CDCD	SeqFlow
albu_sim	0.692 $\pm$ 0.095	0.771 $\pm$ 0.057	0.579 $\pm$ 0.043	0.609 $\pm$ 0.073	0.551 $\pm$ 0.081	0.703 $\pm$ 0.057	0.910 $\pm$ 0.083
amlo_mpo	0.540 $\pm$ 0.021	0.569 $\pm$ 0.029	0.522 $\pm$ 0.026	0.582 $\pm$ 0.024	0.501 $\pm$ 0.021	0.606 $\pm$ 0.068	0.630 $\pm$ 0.051
cele_red	0.307 $\pm$ 0.035	0.366 $\pm$ 0.064	0.304 $\pm$ 0.036	0.452 $\pm$ 0.052	0.316 $\pm$ 0.052	0.402 $\pm$ 0.034	0.645 $\pm$ 0.153
deco_hop	0.588 $\pm$ 0.005	0.610 $\pm$ 0.007	0.586 $\pm$ 0.004	0.617 $\pm$ 0.018	0.583 $\pm$ 0.004	0.615 $\pm$ 0.018	0.866 $\pm$ 0.153
drd2	0.930 $\pm$ 0.090	0.996 $\pm$ 0.006	0.728 $\pm$ 0.247	0.994 $\pm$ 0.012	0.774 $\pm$ 0.210	1.000 $\pm$ 0.000	1.000 $\pm$ 0.000

Appendix

Table 8: Batch regime, coupled generation. Final best score on all 25 PMO objectives after 500 acquisitions (100 initial points, then 100 epochs of 5), mean

\pm

sample standard deviation over 5 seeds. AFGG generates each batch coupled here: one joint qLogEI tilt per candidate batch, so its members are generated aware of each other. Every cell is judged against the opposing group: an AFGG cell against the five baselines, a baseline cell against the two AFGG columns. Bold : the cell’s mean exceeds every mean in the opposing group. Green : that lead is significant, exceeding the cell’s own standard deviation; bold without green therefore means ahead but within noise.

Metric	CDCD	SeqFlow
Validity $\uparrow$	1.0000	0.9972
Uniqueness $\uparrow$	0.9932	0.9862
Novelty $\uparrow$	0.9956	0.9838
FCD $\downarrow$	20.99	6.02

Appendix

Table 9: Unconditional sample quality.

5000

molecules drawn from each generative model and scored against a

30

k-molecule subset of GuacaMol.

	Guidance on vs off
Objective	on	off ( $\lambda{=}0$ )
osim_mpo	0.714 $\pm$ 0.064	0.722 $\pm$ 0.048
med_2	0.199 $\pm$ 0.013	0.173 $\pm$ 0.012
amlo_mpo	0.486 $\pm$ 0.053	0.408 $\pm$ 0.018
zale_mpo	0.420 $\pm$ 0.054	0.431 $\pm$ 0.030
rano_mpo	0.788 $\pm$ 0.012	0.745 $\pm$ 0.026

Appendix

Table 10: Guidance on vs. off; off sets

\lambda=0

, flattening the SNIS weights to uniform so the tilted mean equals the untilted one.

	Guidance estimator
Objective	SNIS (closed form)	REINFORCE
osim_mpo	0.714 $\pm$ 0.064	0.671 $\pm$ 0.033
med_2	0.199 $\pm$ 0.013	0.170 $\pm$ 0.010
amlo_mpo	0.486 $\pm$ 0.053	0.410 $\pm$ 0.062
zale_mpo	0.420 $\pm$ 0.054	0.250 $\pm$ 0.085
rano_mpo	0.788 $\pm$ 0.012	0.687 $\pm$ 0.036

Appendix

Table 11: Guidance estimator: closed-form SNIS vs. REINFORCE (score-function estimator with a leave-one-out baseline) in place of the mean-swap identity.

	Acquisition function
Objective	EI	PI	UCB
osim_mpo	0.714 $\pm$ 0.064	0.708 $\pm$ 0.065	0.696 $\pm$ 0.041
med_2	0.199 $\pm$ 0.013	0.183 $\pm$ 0.015	0.170 $\pm$ 0.009
amlo_mpo	0.486 $\pm$ 0.053	0.495 $\pm$ 0.029	0.490 $\pm$ 0.013
zale_mpo	0.420 $\pm$ 0.054	0.439 $\pm$ 0.045	0.445 $\pm$ 0.055
rano_mpo	0.788 $\pm$ 0.012	0.753 $\pm$ 0.026	0.746 $\pm$ 0.037

Appendix

Table 12: Acquisition function driving both the guidance tilt and candidate selection; UCB uses

\beta=0.1

	Candidate pool size
Objective	1	5	10	20
osim_mpo	0.736 $\pm$ 0.010	0.712 $\pm$ 0.053	0.714 $\pm$ 0.064	0.711 $\pm$ 0.063
med_2	0.186 $\pm$ 0.012	0.192 $\pm$ 0.008	0.199 $\pm$ 0.013	0.204 $\pm$ 0.007
amlo_mpo	0.486 $\pm$ 0.045	0.504 $\pm$ 0.038	0.486 $\pm$ 0.053	0.496 $\pm$ 0.029
zale_mpo	0.432 $\pm$ 0.048	0.411 $\pm$ 0.084	0.420 $\pm$ 0.054	0.406 $\pm$ 0.060
rano_mpo	0.764 $\pm$ 0.008	0.768 $\pm$ 0.019	0.788 $\pm$ 0.012	0.771 $\pm$ 0.015

Appendix

Table 13: Candidate pool size: guided trajectories rolled out per epoch, of which the best by acquisition value is evaluated.

	Where the temperature acts
Objective	power tilt $\gamma{=}2$	scale drift $\lambda{=}0.5$	scale drift $\lambda{=}2$	scale drift $\lambda{=}5$
osim_mpo	0.714 $\pm$ 0.064	0.702 $\pm$ 0.034	0.694 $\pm$ 0.040	0.682 $\pm$ 0.026
med_2	0.199 $\pm$ 0.013	0.194 $\pm$ 0.010	0.191 $\pm$ 0.013	0.182 $\pm$ 0.007
amlo_mpo	0.486 $\pm$ 0.053	0.474 $\pm$ 0.029	0.423 $\pm$ 0.029	0.496 $\pm$ 0.051
zale_mpo	0.420 $\pm$ 0.054	0.449 $\pm$ 0.026	0.402 $\pm$ 0.041	0.401 $\pm$ 0.029
rano_mpo	0.788 $\pm$ 0.012	0.750 $\pm$ 0.031	0.782 $\pm$ 0.023	0.773 $\pm$ 0.023

Appendix

Table 14: Different temperature parameters and how they affect the final generation process.

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Generative models are increasingly central to many de novo discovery pipelines, in which designs are generated at scale and filtered through virtual screens to determine a set of candidates to experimentally validate. While Bayesian optimization (BO) is a natural fit for this setting, as it uses past evaluations to guide future proposals, the computational overhead required for its sequential decision-making becomes a bottleneck when virtual screens are relatively cheap. We make BO practical in this regime by exploiting the unique combination of a linear model constrained to a spherical domain where high-dimensional latents concentrate. We build off recent work justifying the use of linear surrogates, while deriving nearly closed-form solutions to the surrogate modelling and acquisition problems that exploit spherical symmetry. The result is at least a 100x speedup over state-of-the art baselines, with matching or improved performance across molecular and image generation benchmarks. Altogether, our method makes BO a practical drop-in for de novo pipelines where it was previously too slow to consider.

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