Structure-based virtual screening, which includes docking, 9,10 uses the three-dimensional shape of the target protein structure to evaluate candidate ligands. Despite recent examples that successfully use docking to filter 10 8 to 10 10 compounds down to hundreds of interesting compounds, there are inherent drawbacks to this approach. Structure-based screening is limited to targets having reasonably accurate protein structure models, and these methods are far more computationally expensive than ligand-based algorithms with respect to compound throughput. Ligand-based virtual screening evaluates each compound solely based on its chemical properties, which can provide higher throughput. These approaches are applicable to a broader range of targets, including proteins with unknown structures or assays that measure perturbations to pathways, phenotypes, or cell populations. Ligand shape matching algorithms can run faster than traditional docking and scale to current synthesize-on-demand chemical libraries. However, implementations like FastROCS score compounds only based on similarity to individual active reference compounds, ideally in their bound-state conformations, which are not always available. In contrast to supervised machine learning, FastROCS is a shape-based similarity search and does not directly leverage features learned from known inactive compounds.