As digitized paintings continue to grow in popularity and become more prevalent on online collection platforms, it becomes necessary to develop new image processing algorithms to effectively manage the paintings stored in databases. Image retrieval has historically been a challenging field within digital image processing, as it requires scanning large databases for images that are similar to a given query image. The notion of similarity itself, varies according to user's perception. The performance of image retrieval is heavily influenced by the feature representations and similarity measures used. Recently, Deep Learning has made significant strides, and deep features derived from this technology have become widely used due to their demonstrated ability to generalize well. In this paper, a fine-tune Convolutional Neural Network for the artistic genres recognition is employed to extract deep and high-level features from paintings. These features are then used to measure the similarity between a given query image and the images stored in the database, using an Approximate Nearest Neighbours algorithm to get a real time result. Our experimental results indicate this approach leads to a significant improvement in the performance of content-based image retrieval for the task of genre retrieval in paintings.