It has been pointed out that the combination of predictive models generally has a higher reliability than the individual models. In other to identify the best models combination to construct a consensus model, we performed a hierarchical clustering of the models relying on Morgan and RDK fingerprints by comparing their 38129 crossvalidated predictions obtained in the single target validation strategy (vide supra). Jaccard distance was employed as the metric between models and an average linkage was used for the hierarchical clustering calculation as detailed in the Experimental Section. Figure depicts a dendrogram of the hierarchical clustering. Predictions for all models are closely related, with all average distances between groups being lower than 0.1. It should be noted that models relying on the same fingerprint are clustered together before being grouped with models built on a different fingerprint. In the context of each fingerprint, the clustering follows the same order, with models relying on GBT and RF being grouped at first, followed by those built on SVM, FFNN, and k-NN. Based on these findings, the best performing model built on each fingerprint, Morgan::SVM and RDK::SVM, were combined to derive a consensus model. To prioritize the correct identification of active compounds, the consensus model was constructed by combining the predictions of both models so that a compound was predicted as "active" for a given target only if both models agreed in the prediction and "inactive" otherwise. This consensus model showed a mean BA, F1 score, and MCC of 0.835, 0.851, and 0.676, respectively. Wilcoxon signed-rank tests indicated significantly lower values for F1 score than those obtained by the individual models, and no significant difference for BA and MCC values (at 95% confidence level). Since F1 score is defined as the harmonic mean of precision (PPV) and recall (TPR)