Developing optimized AI models for virtual screening requires coordinated selection of algorithms, molecular representations, and data splitting strategies, yet lacks integrated tools. We present PyaiVS, a Python package that integrates nine machine learning algorithms, five molecular representations, and three data splitting strategies. This study demonstrates that constructing efficient AI-driven virtual screening models for small molecules requires coordinated optimization of algorithm architectures (e.g., prioritizing deep learning models such as GCN, GAT, and Attentive FP), molecular representations (ECFP4/MACCS fingerprints for small datasets and molecular graph-based representations for large-scale data), and data splitting strategies (clustering-based splitting achieving 68.5 % optimal AUC-ROC performance). To demonstrate utility, we combined PyaiVS with pharmacophore modeling and docking to screen 4,188,623 compounds for ABCG2 inhibitors. Experimental validation identified four compounds (C1/C6/C7/C9) binding ABCG2 with sub-100 μM kd values (5.31-51.35 μM) that potentiate topotecan cytotoxicity. PyaiVS streamlines virtual screening by unifying critical components into an accessible platform, freely available at https://github.com/danqingmk/OpenVS_PyaiVS.