In a Content-based Image Retrieval (CBIR) System, the task is to retrieve similar images from a large database given a query image. The usual procedure is to extract some useful features from the query image and retrieve images that have a similar set of features. For this purpose, a suitable similarity measure is chosen, and images with high similarity scores are retrieved. Naturally, the choice of these features play a very important role in the success of this system, and high-level features are required to reduce the “semantic gap.” In this article, we propose to use features derived from pre-trained network models from a deep-learning convolution network trained for a large image classification problem. This approach appears to produce vastly superior results for a variety of databases, and it outperforms many contemporary CBIR systems. We analyse the retrieval time of the method and also propose a pre-clustering of the database based on the above-mentioned features, which yields comparable results in a much shorter time in most of the cases.