A small fraction of a large chemical library is docked by conventional means and used as training data for a ML model. The model then either classifies the remainder of the library in "virtual hits" and "non-hits" (DeepDocking ) or aims to predict the docking scores for the entire library (e.g., MolPAL, Glide Active Learning, ). This way, when not every compound needs to be docked, ML-boosted screening approaches can handle ultra-large libraries in a fraction of the time, providing the opportunity to explore the vast chemical landscape of giga-scale libraries.