were recalled consistently during the HASTEN runs by all three replicates (see Figure , black bars and Venn diagrams for iterations 2-10 in Figures S9 and S10 in the Supporting Information). Thus, our case study indicates that a single run of HASTEN is sufficient and no major recall benefit could be gained from repeating experiments with a different initial random selection, at least in a setting with training data on the million-scale. Additionally, the swift convergence into the same selection of compounds indicates that the robustness of the HASTEN approach can still be assumed when stopping on an earlier iteration. Our case study of the two targets SurA and GAK confirmed the applicability of the ML-boosted docking tool HASTEN on the so far unprecedented giga-scale. Our comparison with the corresponding brute-force docking results demonstrated comparable recall for the two distinct targets, identifying, for example, 90% of the true top-scoring 1000 virtual hits, although a modified protocol was necessary in the case of GAK. Given its benefit for the recall in both targets, this novel, modified protocol, which drops failed compounds from the training data, has now been made the default in HASTEN as of version 1.1.