In the rapidly evolving field of artificial intelligence, the importance of data processing and structuring is often underestimated, despite being crucial for developing new models. The integration of search capabilities, particularly through multi-modal models like GPT-4, demonstrates the potential for comprehensive data understanding by processing both text and images. The proposed Search Anything Model unifies natural language, visual property, similarity, and metadata search into a single framework, enabling users to query structured data using natural language. This model leverages computer vision, multi-modal embeddings, and traditional search techniques to facilitate tasks like data exploration, curation, and debugging, as well as enhancing e-commerce cataloging by interpreting and categorizing product images and descriptions. Encord's Encord Active platform empowers users to interact with visual data through natural language, offering customizable search capabilities to meet specific needs by integrating or fine-tuning custom embedding models. The adoption of Natural Language Search (NLS) is transforming data interaction across various fields, enhancing productivity and unlocking the potential of data by providing intuitive and efficient methods to explore, curate, and debug datasets.