A vector stream can be modeled as a sequence of pairs ((v1, t1). . .(vn, tn)), where vk is a vector and tk is a timestamp such that all vectors are of the same dimension and tk less than tk+1. The vector stream similarity search problem is defined as: Given a (high-dimensional) vector q and a time interval T, find a ranked list of vectors, retrieved from a vector stream, that are similar to q and that were received in the time interval T. This dissertation first introduces a family of vector stream similarity search methods that do not depend on having the full set of vectors available beforehand but adapt to the vector stream as the vectors are added. The methods generate a sequence of indices that are used to implement approximated nearest neighbor search over the vector stream. Then, the dissertation describes an implementation of a method in the family based on Hierarchical Navigable Small World graphs. Based on this implementation, the dissertation presents a Classified Ad Retrieval tool that supports classified ad retrieval as new ads are continuously submitted. The tool is structured into a main module and three auxiliary modules, where the main module is responsible for coordinating the auxiliary modules and for providing a user interface, and the auxiliary modules are responsible for text and image encoding, vector stream indexing, and data storage. To evaluate the tool, the dissertation uses a dataset with approximately 1 million records with descriptions of classified ads and their respective images. The results showed that the tool reached an average precision of 98 percent and an average recall of 97 percent.