Third, exhaustive, structure-based scoring of a tera-size or larger library against a therapeutic target site is beyond today's computational capabilities. Giga-sized vHTS docking campaigns have been reported , but require vast resources and their techniques do not scale with the rapid growth of CSLs. Sampling-based methods that extrapolate scores using similarity measures or surrogate models from a docked subset of the CSL are resource-efficient but suffer from limitations related to increasingly sparse and incomplete representations of the CSL by the subset . This limitation will worsen as CSLs continue to grow exponentially. Alternatively, divide-and-conquer approaches can construct high-scoring compounds by incrementally adding synthons in subsequent docking or shape similarity iterations and pruning low-scoring fragments. While this method relies on the same combinatorial scaling techniques that underlie the CSL, a fragment-based pruning approach has several shortcomings. Importantly, pruning choices must be made with a high degree of uncertainty stemming from incomplete compounds. For example, even minor chemical changes in growing fragments can lead to altered binding modes . Alternatively, a large fragment can score favorably at an intermediate iteration, despite steric hindrance preventing its growth. That could eliminate a smaller fragment ultimately satisfying new and favorable interactions with the addition of more fragments (Figure ). Another important limitation of this approach is that several key compound properties in hit discovery, notably solubility, are not additive across chemical groups, reducing predictive efficiency (Figure ). Thompson sampling in reagent space offers important advantages . However, the assumption of independence across reagents restricts the method's ability to learn covariance, critical for capturing non-additive properties. Traditional search-and-predict approaches unavoidably will remain challenged by growing CSLs.