How to optimally represent protein conformational states computationally, and how to generalize docking scores to (unseen) conformational substates remain important open problems. Some, but not all, proteins with ligands in the Protein Data Bank (PDB) are structurally resolved in multiple conformations. For individual systems, sophisticated and resource intensive protocols including molecular dynamics simulations and Markov State Models can access biologically relevant conformations , but these methods scale poorly to large databases of thousands of receptors. Time-independent sampling is a less resource-intensive alternative to access conformational substates. A common limitation of screening with multiple conformations, of any origin, is the challenge in comparing docking scores for molecules docked to different conformations. This has limited the adoption of earlier ensemble approaches, but machine learning techniques can mitigate this problem . Importantly, conventional docking and scoring protocols cannot internalize receptor conformational variability encoded in the thousands of structurally resolved receptor-ligand complexes in, for example, the PDBbind dataset. While DL-approaches are relatively underexplored in ensemble docking, they can, in principle, learn and generalize how receptor flexibility accommodates distinct binding modes, both of cognate and non-cognate ligands.