In contrast to ML-based methods, DL-based methods avoid the requirement to specify features, and instead learn relevant features directly from structural representations of the protein-ligand complex. In recent years structure-based DL architectures have enabled high-throughput vHTS and contributed to the discovery of numerous new leads for drugs, often for challenging protein targets and diseases . However, early structure-based DL approaches appeared to learn molecular features from a pose-free structure-based descriptor only, neglecting ligand binding modes and protein ligand interactions . This is especially detrimental to predicting affinity for ligands docked to non-cognate receptors, which is a typical use-case in structure-based drug discovery (SBDD). To enforce sensitivity to protein-ligand interactions, more recent DL vHTS approaches include ligand binding mode information, either as a feature or as a training label in a multi-task architecture . Simultaneous learning on ligand binding modes substantially improved activity screening and led to better generalization beyond the training set. Importantly, predicting correct ligand binding modes has merit in its own right. Precise protein-ligand interactions are vital for developing structure activity relationships in hit-to-lead and leadoptimization applications downstream from vHTS. The efficacy of structure-based virtual screening campaigns relies on an adequate representation of the protein conformational ensemble. Proteins and their ligands undergo conformational exchange under physiological conditions , and in many cases ligands bind through induced fit (Fig 1A) or bind short-lived intermediate states through conformational selection (Fig ) . In those situations, the binding site of a crystal structure may be partially or even fully occluded in the absence of a ligand, hindering the discovery of potent binders. In cases where the protein's cognate ligand has small molecular weight, a holo crystal structure would limit opportunities for docking larger compounds even though those could be accommodated by the protein's full conformational ensemble (Fig ). Similarly, structurally uncharacterized disease mutations distal to the ligand binding site can shift the protein conformational ensemble, dramatically reducing or even depleting populations favorable for ligand binding in wild-type protein in vivo (Fig 1D ) . Such a situation would manifest itself by unfavorable binding kinetics in experimental assays despite highly ranked compounds in the virtual screen. Screening against multiple receptor conformations can fail to enrich experimentally validated active compounds if the conformations are higher-energy and unlikely to be accessible in solution. Careful selection of receptor states can mitigate these effects of protein dynamics and increase virtual screening performance .