In summary, we showed that in certain docking scenarios, the complete removal of failed compounds from the training data appears to improve the model quality. Depending on the case, it can also greatly enhance the recall (GAK) or show only a minor impact on the recalled compounds (SurA). In particular, dropping failed compounds is likely beneficial when a large proportion of evaluated compounds fail to dock successfully. This protocol modification can also add to the speed-up of model training, as there is less data to process. Our case study identified the treatment of failed compounds or their assigned score as factors that can improve recall in ML-boosted screening campaigns with HASTEN/Chemprop.