Area under the receiver operating characteristic (ROC) curve (from now, referred to as AUC), traditionally employed for measuring the performance of classification models, has been reported for not being enough for evaluating virtual screening models because it is not sensitive to early recognition and it is a↵ected by class imbalance. Thus, we complement this information with partial AUC (pAUC) at 5%, which allows to focus on the region of the ROC curve more relevant for virtual screening 1 (up to 5% of the False Discovery Rate), with Cohen's kappa coe cient (), which measures the agreement between real and predicted classification, and with the Boltzmann-enhanced discrimination of receiver operating characteristic (BEDROC), a metric proposed to overcome the limitations of AUC 57 increasingly popular in the evaluation of virtual screening models. BEDROC uses an exponential function based on parameter ↵ and is bounded between 0 and 1, making it suitable for early recognition. As recommended by Riniker et al, we focus on AUC and BEDROC with ↵=20, whilst also reporting ↵=100.