Reliance on MD calculations for the ground truth to train the active learning models allows one to perform ligand optimization completely in silico. While docking would be a faster alternative, MD calculations provide a much more accurate measure of the binding free energy, one that explicitly takes entropic contributions into account. Nevertheless, MD still suffers biases due to force field errors and uncertainties from incomplete sampling of phase space. While this approach can eliminate the need for effort intensive ligand synthesis and experimental affinity measurements during the course of the ligand optimization process, affinities of the final ligand selections still need to be experimentally validated.