The integration of artificial intelligence (AI) into molecular docking and virtual screening has revolutionized drug discovery by addressing long-standing challenges in target flexibility, binding conformation prediction, and computational efficiency. Traditional molecular docking, limited by semi-flexible approaches, struggles with the dynamic nature of protein-ligand interactions. AI-driven methods, including machine learning and deep learning, enhance prediction accuracy by leveraging ensemble docking strategies and advanced scoring models. Techniques like naïve Bayesian models, random forest algorithms, and convolutional neural networks enable effective ensemble docking, improving the handling of target flexibility and ranking diverse protein conformations. Virtual screening efficiency has similarly advanced through AI frameworks such as DeepDock and MEMES, which employ active learning and Bayesian optimization to navigate ultra-large chemical libraries rapidly. These innovations significantly reduce computational time while maintaining high predictive accuracy. Despite these achievements, challenges persist in generalizability, binding mode prediction, and application to underexplored proteins. This chapter examines the transformative impact of AI on molecular docking and virtual screening, discussing state-of-the-art methodologies and future directions to overcome remaining barriers in computational drug discovery.