Abstract: With the rapid growth of data in the field of drug discovery and the development of artificial intelligence(AI) technology, the application of computer aided drug design and AI assisted drug design in drug screening is becoming increasingly widespread. This article reviews the application of virtual screening technology and AI in drug discovery, with a particular focus on ligand-based virtual screening, structure-based virtual screening, and AI-driven virtual screening technologies. Through an in-depth analysis of the strengths and limitations of these technologies, the article points out that AI technologies, especially the introduction of machine learning and deep learning algorithms, have greatly improved the accuracy and efficiency of virtual screening, while also facing challenges such as data quality, model complexity, generalization ability, and interpretability. Future research should focus on improving model performance while optimizing its generalization ability and transparency to ensure the effectiveness and reliability of the technology in practical applications. Additionally, AI technologies such as deep generative models demonstrate the potential to break traditional boundaries in exploring the chemical space of drug discovery, heralding revolutionary changes in the field of drug discovery and molecular design.