With the number of photos people take growing, it’s getting increasingly difficult for a common person to manage all the photos in its digital library, and finding a single specific photo in a large gallery is proving to be a challenge. In this thesis, the MyWatson system is proposed, a web application leveraging content-based image retrieval, deep learning, and clustering, with the objective of solving the image retrieval problem, focusing on the user. MyWatson is developed on top of the Django framework, a high-level Python Web framework, and revolves around automatic tag extraction and a friendly user interface that allows users to browse their picture gallery and search for images via query by keyword. MyWatson’s features include the ability to upload and automatically tag multiple photos at once using Google’s Cloud Vision API, detect and group faces according to their similarity by utilizing a convolution neural network, built on top of Keras and Tensorflow, as a feature extractor, and a hierarchical clustering algorithm to generate several groups of clusters. Besides discussing state-of-the-art techniques, presenting the utilized APIs and technologies and explaining the system’s architecture with detail, a heuristic evaluation of the interface is corroborated by the results of questionnaires answered by the users. Overall, users manifested interest in the application and the need for features that help them achieve a better management of a large collection of photos.