The discovery of new molecules is essential for technological advancement, with applications such as batteries and medicine being examples of areas that directly benefit. Machine Learning techniques have a huge potential applications in this context. Moreover, in many cases, if we are able to rank molecular structures, it is not necessary to know the exact value of a molecular property. In other words, a ranker model can be useful for molecular screening. In this work, we developed a deep learning model to rank molecular structures, we used a siamese network approach and pairwise learning to learn the ranking. According to different properties from QM7-X dataset, the results show that the performance of the learn to rank siamese architecture is similar to or better than standard regression model point-wise training.