The discovery of small molecules that bind a targeted protein with high affinity is a key early step in many drug discovery projects. This ligand-discovery step can take several years, and its cost on a per launched compound basis rivals that of later-stage clinical trials . Various computational methods have therefore been developed that are aimed at speeding this step and lowering its cost. These methods include structure-based approaches, in which the three-dimensional structure of the targeted protein, usually obtained by X-ray crystallography, is used to guide the discovery of a tight-binding ligand. In one common paradigm, known as virtual compound screening , a library of available candidate ligands is computationally docked to the targeted binding site, and compounds with favorable docking scores are procured and tested experimentally. Although such docking calculations can significantly enrich the yield of compounds that bind the targeted protein, i.e., of actives, relative to a random selection of compounds , the accuracy of docking is limited by the simplifying approximations used to achieve high computational speed. For example, the protein is usually treated as largely rigid, solvent is treated rather crudely, and the entropic and energetic consequences of conformational fluctuations are handled implicitly at best.