Vector similarity offers a powerful method for data exploration that extends beyond traditional full-text search techniques, enabling the discovery of hidden patterns and insights within datasets. By leveraging tools such as dimensionality reduction, clustering, and visualization, users can navigate data spaces more effectively and uncover deeper insights. Techniques like diversity sampling enhance data discovery by introducing innovative ways to explore data. In practical applications, such as improving the quality of text-and-image datasets for online marketplaces, similarity search helps identify errors and refine data quality, demonstrating its value across various domains.