When considering DNNs, the situation is similar (Figure ). LSTAR, ASP and RAD2D are the closest to the "best" baseline across all datasets, while PH2 and PH3 perform the worst (Table ). ASP performs ). For example, ECFP achieves most frequently the best performance in terms of PR-AUC (3/13 datasets), while it never outperforms all other fingerprints in terms of recall. When considering the post-hoc statistical tests, almost all methods are equal in terms of recall, while there are many significant differences in terms of MCC, PR-AUC and ROC-AUC compared to RF (Figure ). This finding suggests that the DNNs are more susceptible to the choice of encodings than decision tree ensembles.