In our test cases, we achieved highly similar RMSE values for validation and test sets (see Figure and Tables and), suggesting good generalizability. It can further be seen that depending on the protocol and target, RMSE values of individual ML iterations converge well before the final iteration 10. Similarly, recall curves (see Figure ) also often show an earlier convergence. Furthermore, the vast majority of the top compounds' chemical diversity is already found on early iterations and the predicted scores of the true top-scoring virtual hits are closely correlated even on earlier iterations, as shown exemplarily for the SurA target in Figures and. Thus, if speed is of the essence, our results underline that HASTEN could, for example, be stopped already at iteration 5, which doubles the speed-up achieved by ML-boosted docking while often sacrificing less than 5% in recall of the top-scoring compounds. This is further supported by a recent screening campaign performed by Orion Pharma, combining HASTEN with Glide SP to screen a 4.1 billion compound version of Enamine REAL against an oncology target. In this case, only two 0.1% iterations were performed and yielded approximately 100 000 high-scoring virtual hits (estimated recall based on the initial random sampling around 0.5, data not shown). Additional speed-up could likely be gained from the use of smaller docking fractions in the generation of the training data. To study this possibility, we re-ran HASTEN for both targets, this time adding only 0.01% of the compound library per iteration as training data (i.e. 156 000 compounds per iter.). Indeed, ten iterations of these runs proved around 2-3 times faster than our original runs with a docking fraction of 0.1%. Recalls of 80%, 68%, and 62% of the true top 100, 1000, and 10 000 virtual hits were achieved for SurA, and 84%, 73%, and 60% for GAK, respectively (using the novel drop-failed protocol for both targets, see also Tables and). While ten iterations with a larger docking fraction allowed for higher recalls (SurA: 96%, 91%, and 87% and GAK: 94%, 90%, and 84% of the true top 100, 1000, and 10 000 virtual hits, respectively), the runs with smaller training data were still able to recall the bulk of the top-scoring compounds.