(2) The ML models are tailored to specific protein targets, rendering them non-transferable across different binding pockets. Although there have been attempts to develop general ML models capable of predicting scores across multiple proteins, their accuracy is worse than target-specific models, and performance degrades when applied to unseen proteins. Consequently, to achieve accurate results, researchers must repeat the entire active learning workflow for each new protein target. This requires tuning, training, and validating models for every protein binding site, which requires further expertise and additional computational overhead.