The TREC 2019 Deep Learning task aims at studying information retrieval in a large training data regime. It includes two tasks: the document ranking task (1) and the passage ranking task (2). Both of these tasks had a full ranking (a) and reranking (b) subtasks. The SIB Text Mining group participated at the full document ranking subtask (1a). In order to retrieve pertinent documents in the 3.2 million documents corpus, our strategy was two-fold. At first, we used a BM25 model to retrieve a subset of documents relevant to a query. We also tried to improve recall by using query expansion. The second step consisted in reranking the retrieved subset using an original model, so-called query2doc. This model, which has been designed to predict if a query-document pair was a good candidate to be ranked in position #1, was trained using the training dataset provided for the task. Our baseline, which is basically a BM25 ranking performed the best and achieve a MAP of 0.2892. Results of the query2doc run clearly indicates that the query2doc model could not learn any meaningful relationship. More precisely, to explain such a failure, we hypothesize that using documents returned by our baseline model as negative items confused our model. As future steps, it will be interesting to take into account features such as the document’s BM25 score as well as the number of times a document’s URL is mentioned in the corpus and use them along with learning to rank algorithms.