Search at the Cost of Sampling: Nearly-Instant Latent Space Bayesian Optimization

This paper addresses the computational bottleneck of Bayesian optimization when applied to de novo discovery pipelines with cheap virtual screens. The authors propose a nearly-instant approach that uses sampling costs instead of traditional sequential decision-making overhead. While the method is general, it is particularly relevant to materials and molecular discovery workflows where generative models produce large candidate sets for screening.