High-throughput screening (HTS) is widely used in modern drug discovery. It enables batch activity testing of compounds and provides important support for the identification of active compounds. However, its screening efficiency and accuracy need to be improved. To address this issue, artificial intelligence (AI) has been gradually integrated into the HTS workflow. Leveraging the advantages of machine learning (ML) and deep learning (DL), AI optimizes applications in structure-based and ligand-b