Although BA, F1 score, and MCC are well-suited metrics for model performance estimation on imbalanced datasets, in a practical medicinal chemistry application, the correct identification of active compounds is often more important than the correct identification of inactive ones. To this end, the performance of the individual models and the derived consensus model were studied in terms of PPV, TPR, negative predictive value (NPV), and true negative rate (TNR). To estimate the models' applicability domain, these metrics were computed on a distance-to-model (DM) basis as detailed in the Experimental Section. All cross-validated predictions were categorized into four quartiles (Q1-Q4) according to their mean Jaccard distances to the training set in the context of each target (Table in the Supporting Information). Summary results of the three models' performance are presented in Table , and their distribution across the 55 epigenetic targets is shown in Figure . , and their distribution across the 10 samples is shown in Figure . Under this validation strategy, PPV and TPR showed the same trends as in the single target validation: both decreased as the DM increased for all models. Wilcoxon signed-rank tests indicated significantly lower TPR values and higher PPV values for all quartiles when comparing the consensus model to any of the two individual models (at 95% confidence level)