An important aspect in the development of scoring functions and other machine learning-based models for drug discovery is the concept of data leakage. Data leakage is a significant bias that has recently gained considerable attention in the literature on machine-learning models for drug discovery. This bias has been described for widely used datasets like the PDBbind. It occurs when samples in the training set are highly similar to those in the validation and test sets, leading to an overestimated predictive capacity. Such models can exhibit poor generalisation when evaluated on new instances with low similarity (e.g., structural or sequence similarities) to the training samples. In this sense, the core set, widely used as benchmarking data for evaluating docking and scoring methods, exhibits a significant data leakage bias, potentially leading to overestimated accuracy metrics . Evaluating the developed machine-learning models on prospective and independent datasets is essential to ensure a comprehensive assessment of their predictive accuracy beyond the core set.