This oversight in rigorous evaluation standards is particularly evident in the selection of commonly used benchmarking datasets. One such benchmark is the Comparative Assessment of Scoring Functions, 2016 (CASF-2016) . It has been demonstrated in numerous studies that several ML/DL models developed using this benchmark exhibit a significant dependency on the inherent similarity between training and test set examples. This "data leakage" results in inflated performance metrics that do not necessarily translate to true predictive power outside the studied dataset . The CASF-2016 has been instrumental in revealing these tendencies, but also highlights the need for more robust methods of separating training and validation datasets to prevent model overfitting and ensure genuine generalizability.