The exponential growth of data lakesData lakes has made efficient table discovery a critical challenge. This chapter explores table search in data lakes, covering methods, indexing techniquesIndexing techniques, research challenges, and future directions. We break down the complexities of table searchTable search into three foundational pillars: table understandingTable understanding, search mechanisms, and indexing strategies. Table understanding, which includes table annotationTable annotation and domain discoveryDomain discovery, forms the semantic backbone for accurate retrieval. We thoroughly examine three core paradigms of table search, keyword-basedKeyword-based, joinableJoinable, and unionableUnionable searches, each playing a vital role in tasks such as data augmentation and integration. To enhance search efficiency, we analyze advanced indexing techniques, including tree-basedTree-based, hash-basedHash-based, graph-basedGraph-based, and quantization-basedQuantization-based approaches, emphasizing their scalability and performance benefits. Beyond methodologies, we highlight key open research challenges, such as defining table relatedness, improving semanticSemantic search accuracy, and establishing robust benchmarks. By integrating these perspectives, we demonstrate the interconnected nature of table understanding, search, and indexing, showcasing their collective impact in unlocking the analytical and integrative potential of data lakes. This chapter not only provides a comprehensive framework for table search but also sets the stage for future advancements in the field.