This chapter explores the role of explainable artificial intelligence (XAI) in drug discovery, detailing key explainability techniques and their applications in drug design. We present a historical perspective on computational drug discovery, followed by a taxonomy of state-of-the-art XAI methods, discussing their benefits and limitations. Practical guidelines are provided to aid in selecting the most suitable explainability techniques based on the given models and data types. Additionally, we review real-world case studies where XAI enhances AI-driven drug discovery, improving model reliability and facilitating rational design. Finally, we highlight emerging research directions to advance explainability in AI-driven pharmaceutical innovation.