Accurate binding affinity prediction to given protein-ligand complexes is of the greatest importance for scoring function in structure-based virtual screening (SBVS) method. The recent progress of machine learning (ML) has enabled great advancements in ML-based scoring function development. Such data-driven binding affinity prediction approaches heavily rely on data scale and quality of complex crystal structures, 5,6 limiting their performance and applicability since the current crystal structure databases contain insufficient chemical and protein structure spaces (i.g., 22,920 protein-ligand complexes in PDBbind 2024 7 ). A more general field -compound-protein interaction (CPI) prediction -has gradually gained researchers' attentions since it focuses on directly predicting binding affinities or bioactivities for protein-ligand pairs without complex structure required. Such structure-free CPI methods are much flexible than ML-based scoring functions with substantially larger-scale CPI data available for model training (i.g, ~1.6M assays in ChEMBL 8 and 1.1M binding measurements in BindingDB 9 ). Some recent studies demonstrated that well-trained CPI models can beat traditional protein-ligand binding affinity prediction methods (e.g., Vina, 10 Gnina, 11 RFScore, 12 ∆VinaRF, 13 etc.) regarding both prediction accuracy and screening power. However, the availability of a vast amount of bioactivity data for training CPI models presents both opportunities and challenges.