In this paper, we present a novel machine learning-based image ranking approach using Convolutional Neural Networks (CNN). Our proposed method relies on a similarity metric learning algorithm operating on lists of image examples and a loss function taking into account the ranking in these lists with respect to different query images. This comprises two major contributions: (1) Rank lists instead of image pairs or triplets are used for training, thus integrating more explicitly the order of similarity and relations between sets of images. (2) A weighting is introduced in the loss function based on two evaluation measures: the mean average precision and the rank 1 score. We evaluated our approach on two different computer vision applications that are commonly formulated as ranking problems: person re-identification and image retrieval with several public benchmarks and showed that our new loss function outperforms other common functions and that our method achieves state-of-the-art performance compared to existing approaches from the literature.