There are various approaches to tackle the computational expense associated with billion-scale structure-based virtual screening. One strategy is to grow compounds from fragments instead of docking full-size molecules and thus avoiding the enumeration of large numbers of compounds. Alternatively, several strategies that recently gained traction for boosting docking-based screening rely on iterative approaches utilizing machine learning (ML). The idea is simple, yet powerful: