Generally speaking, compounds that are highly dissimilar from all the compounds used in the model-training cannot be predicted reliably (Tropsha, 2010), thus the applicability domain of our models should be defined in accordance with the guideline of Organization for Economic Cooperation and Development (OECD). In this work, each compound in the test set (Dataset-Test) is compared with every compound in the cross-validation dataset (Dataset-CV) according to the Tanimoto similarity based on 2048bit-ECFP6 fingerprint due to its more structural features and less bit collisions. Subsequently, five most similar compounds from Dataset-CV are retrieved and treated as five nearest neighbors for the given compound in Dataset-Test, and the average of five similarities is defined as the “average-similarity” between this given compound and these five nearest neighbors. It should be noted that five nearest neighbors are selected here, because the optimal nearest neighbors is five for the best KNN model with full 2048bit-ECFP6 (M0255 in Table S1) based on the highest F1-score (0.927).