The popularity of social networks and web-based personal image collections has resulted to a continuously growing volume of publicly available photos and videos. Users are uploading, describing, tagging and annotating their personal photos. Moreover, a recent trend is to also ”geotag” them, that is to mark the location they were taken onto a web-based map. Consequently, this growth of image collections has created the need for fast, robust and efficient systems, able to analyze large-scale diverse and heterogeneous visual content. This growing need for automatic metadata generation, concept detection, search and retrieval has boosted research efforts towards these directions. The work presented herein is a web-based system that aims not only to the retrieval of visually similar images, but also to determine the location they were taken by exploiting the available socially created metadata. This system makes use of a visual vocabulary and a bag-of words approach, in order to describe the visual properties of an image. Moreover, geometric constraints are applied, in order to extend the bag-of-words model towards more accurate results. We begin by describing some related work in the field of image retrieval, in order to present both the relation and the novelties of the presented system in comparison with the existing techniques.