Successful drug discovery campaigns rely on identifying biologically active lead molecules that are chemically distinct from known compounds for the disease target. This is especially challenging when there is little or no nearby ligand data available, as is the case with novel targets, or when novel scaffolds are distant in chemical space. Structure-based, virtual High-Throughput Screening methods are designed to overcome this challenge, by identifying novel compounds with predicted activity from vast chemical libraries, for example, MCULE 1 or ENAMINE. vHTS is routinely applied as a first step in the drug discovery process, with hit rates surpassing those of experimental screens. Conventional, structure-based vHTS approaches use an empirical or force-field based scoring function to dock distinct ligand poses to a mostly rigid receptor and predict affinity. Underlying structure-based vHTS approaches is the assumption that receptor-ligand binding poses correlate with experimentally observed affinities. While conventional approaches have led to several exciting results, including a potent inhibitor for AmpV β-lactamase , they have several important drawbacks. First, identifying a scoring function that simultaneously gives high docking power (distinguishing correct docking poses from decoy poses) and high scoring power (generating an affinity score) has historically been challenging . For example, the widely-used molecular docking scoring function AutoDock-Vina 8 excels at pose reproduction but is less competent at correlating poses to affinity, as assessed in the CASF benchmark . Second, conventional methods bear substantial computational cost. Consequently, their ability to predict affinity is limited to small or medium-sized chemical libraries of tens to several hundred million compounds. Third, the dynamic nature of proteins is exploited in ligand binding . Different receptor conformations can bind different ligand chemotypes, perhaps best exemplified by the 'DFG-in' and 'DFG-out' states occupied in different ratios by many protein kinases . Docking a ligand to a rigid receptor conformational substate that deviates from its native bound state (e.g., an apo state) can result in inaccurate predictions of the bound complex that are not useful for further drug design applications. Unfortunately, incorporating receptor flexibility and representing binding-competent receptor conformations remains challenging in conventional methods and substantially increases computational cost.