Rapidly prioritising which compounds to make is a key question in early-stage drug discovery . When the structure of the target is known, one commonly-used approach is highthroughput protein-ligand docking, 2,3 which uses scoring functions to rank compounds by their predicted affinity for the target protein. Such scoring functions must make several assumptions that trade biophysical accuracy for speed, and rely predominantly on structurebased features to quantify the various protein-ligand interactions. It is common practice to use X-ray crystallographic structures of bound protein-ligand complexes when training and testing these models to predict protein-ligand binding affinity. This is reasonable, as it isolates the task of binding affinity prediction from structural errors that might be introduced as a result of inaccurate or incorrect ligand pose prediction, or due to the rigid receptor assumption that is often used in docking. However, in a real-world drug discovery scenario, it is highly unlikely that a crystal structure of the bound protein-ligand complex will be available for every ligand or class of ligands of interest. Instead, protein-ligand docking is often used to predict the binding mode of each ligand within the binding pocket of a protein target. Thus, for scoring functions to be relied upon in prospective screens, they will need to show good predictive performance not only on experimentally-determined binding poses, but also on docked poses.