1Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, PA, USA, 2Department of Biochemistry and Biophysics, Washington University in St. Louis, St. Louis, MO, USA, 3Department of Pharmaceutical Sciences, University of California Irvine, Irvine, CA, USA. Obtaining accurate binding free energies from in silico screens has been a longstanding goal for the computational chemistry community. However, accuracy and computational cost are at odds with one another, limiting the utility of extant approaches. Many methods achieve scale by assuming that the target protein adopts a single structure, or undergoes limited fluctuations around that structure. Others simulate each protein-ligand complex of interest, accepting lower throughput in exchange for better predictions of binding affinities. Here, we present the PopShift framework for accounting for the ensemble of structures a protein adopts and their relative probabilities. Protein degrees of freedom are enumerated once, then molecules can be screened against this ensemble posthoc. Specifically, we use Markov state models (MSMs) as a compressed representation of a protein’s thermodynamic ensemble. We start with a ligand-free MSM and then calculate how addition of a ligand shifts the populations of each state based on the strength of the interaction between that protein conformation and the ligand. Here we use docking to estimate the affinity between a protein structure and ligand, but any estimator of affinity could be used. We test PopShift on the benchmark pocket T4 Lysozyme L99A. We find that PopShift is more accurate than common strategies, such as docking to a single structure and traditional ensemble docking—producing results that compare favorably with alchemical binding free energy calculations in terms of RMSE but not correlation. PopShift also provides the shifted probability of the protein’s conformations across a range of ligand concentrations, thereby enabling the analysis of allosteric effects of ligand binding. Thus, we expect PopShift will be valuable for hit finding and for providing insight into phenomena like allostery.