Here, we introduce a DL-based method for binding mode prediction that exploits protein conformational ensembles instead of single structures in vHTS applications. Starting from a protein crystal structure or homology model, we use Rosetta's comparative modeling protocol (RosettaCM) to sample low-energy conformations near the crystal structure to generate a sixmember conformational ensemble, which includes the starting structure. We applied this protocol to each receptor-compound pair in PDBbind v2019 to generate FlexPDBbind v2019, a curated data set of conformational ensembles of drug targets. To enrich the quality of compound poses docked to these ensembles we adapted AtomNet GRAPHite, a new directional message passing graph convolutional network, for Pose Ranking applications, which we describe as AtomNet PoseRanker. To maximize learning on distinct receptor conformations, we additionally trained AtomNet PoseRanker on non-cognate poses, obtained from cross-docking ligands to distinct crystal structures of the same protein (uniprot identifier) in the PDBBbind v2019 dataset.