while MHFP excels at modeling the antitumor dataset (0.89 ROC-AUC, 0.82 PR-AUC). By inspecting the second principal component, we can conclude that MAP4, MHFP and LSTAR tend to have less false positives, while PubChem, MACCS and ESTATE generate less false negatives (Figure ). Both pharmacological fingerprints have poor performance (Table ), achieving only 0.66 and 0.68 average ROC-AUC scores for PH2 and PH3 respectively. This is likely related by their lack of structural discriminative power observed when evaluating the distribution of pairwise Jaccard-Tanimoto similarities. When evaluating the performance on the individual datasets, the best fingerprints change depending on the target property and metric. For example, RAD2D obtains the highest PR-AUC in 4 datasets out of 13, followed by ASP with 3. In terms of MCC instead RAD2D, DFS and FCFP are the most promising, achieving the best performance in 2 datasets out of 13 each. When considering the post-hoc pairwise comparison tests, the situation differs from metric to metric (Figure ). Most fingerprints have statistically significant differences when considering precision and specificity, while they are more comparable in terms of recall. This indicates that the false positive rate of RF models is significantly affected by the choice of molecular encoding. When considering MCC, ROC-AUC and PR-AUC, PH2, PH3