The behavior of the active learning protocol trained on MD free energies was similar to the models trained on experimental free energies in the retrospective analysis in Part I. The protocol initially explored the chemical space to find promising high affinity regions (Fig. ). After the switch to using the best predicted ligands, greedy selection, from models based only on the 2D_3D ligand representation in the third iteration, the protocol predominantly focused on one region of chemical space. In this subspace, a substitution of a benzene ring directly bound to the scaffold was preferred, and probing molecules with this chemistry returned higher affinity hits (Fig. ). Finally, at the fifth iteration, the number of found high affinity ligands dropped, indicating their pool was likely exhausted. Therefore, the protocol was stopped at this point.