Foundation Model Informed Acquisition Functions for Molecular Discovery

Bayesian optimization (BO) is widely used to accelerate molecular discovery by reducing costly oracle evaluations. Foundation models provide a promising source of prior knowledge for BO, but simply using their representations in standard surrogate-based pipelines is often unreliable in low-data regimes with high-dimensional features and vast discrete candidate spaces. Rather than asking whether LLMs are universally useful for molecular BO, we ask how Acquisition Functions (AFs) should be designed to exploit weak, high-dimensional, and partially informative foundation-model priors. We propose \emph{LLM-guided Acquisition Tree} (LLMAT), a surrogate-free BO framework that reformulates likelihood-free acquisition estimation as recursive local acquisition learning. LLMAT trains binary classifiers on LLM representations, where each classifier jointly induces a promising/non-promising partition and defines a local AF within the corresponding region. This produces a hierarchy of localized AFs and enables efficient candidate selection via Monte Carlo Tree Search. To stabilize learning with few observations, LLMAT meta-learns shared classifier parameters and initializations across tree nodes. An optional LLM-guided clustering module further reduces AF evaluation cost by restricting search to statistically promising coarse property clusters. Extensive experiments and ablations demonstrate substantial improvements in scalability, robustness, and sample efficiency for LLM-guided BO in molecular discovery.