This tutorial presents advanced methods for efficiently searching and exploring large visual datasets, addressing the increasing demand for high-performance visual retrieval systems as multimedia content grows exponentially. We will begin by covering the principles of large visual encoders, emphasizing techniques for generating and improving compact, high-quality general-purpose visual descriptors. Next, we will explore strategies to enhance cross-modal retrieval. Participants will gain insights into approximate nearest neighbor search methods, with a focus on graph-based approaches that maximize search efficiency in dynamic datasets. In addition, the tutorial introduces innovative visualization techniques for high-dimensional data, such as grid-based sorting, which enable intuitive navigation and exploration of extensive image collections. Hands-on exercises provide practical experience with the concepts discussed using Jupyter notebooks and interactive demonstrations. This tutorial will provide researchers, practitioners, and students in information retrieval, computer vision, and data science with basic and advanced skills to efficiently search and explore large visual datasets.