We hypothesize that the large fraction of failed compounds in the training data drives the learning process in the ML step towards primarily identifying failed compounds to minimize the chosen metric (RMSE) rather than picking up smaller differences between the successfully docked compounds. Importantly, since failed compounds share features with those that dock successfully and because failure to dock can be an artifact of the docking methodology, the model can only imperfectly predict which compounds will fail. As a consequence, when their failed score is far outside the distribution of scores from successful dockings (see Figure ), this will drive the model into associating higher, less favorable scores with features observed in failed compounds.