In computer-aided drug design (CADD), virtual screening is a fundamental approach in which large compound libraries are rapidly filtered through multiple iterative rounds to identify hits. Typically, this process involves three main tasks: pose prediction, binding affinity or docking score assessment, and bioactivity prediction, all of which are facilitated by scoring functions (SFs) . Traditionally, SFs have employed physics-based, empirical, or knowledgebased methods . However, the recent rise of machine learning (ML) and deep learning (DL) has shifted the focus towards proteochemometric models (PCMs) that utilize both protein and ligand descriptors to model the ligand-target interaction space 3 4 5 6 7 8 . Despite the growing dominance of ML/DL in PCMs, often serving as complex scoring functions, the rigorous evaluation standards essential for their validation are frequently overlooked.