Address matching plays a very important role on the daily activities of post offices and companies responsible for processing and delivering packages. Address matching is a subtask of geocoding, and consists in pairing addresses, from multiple databases, that refer to the same place. Geocoding aims to assign physical coordinates (latitude and longitude) to an address so that the routes performed by the delivery-man can be planned accurately. Errors in the address matching are quite harmful to this type of companies, in economic, environmental, or reputational terms. There are several methodological approaches to perform address matching. Some methodologies involve doing a standardization of the address or even parsing the elements of the address, to then perform elementwise matching. These methodologies are not perfect and end up needing the manual correction of the address by a human. This dissertation contributes to the solution of this problem by presenting a model that executes with success and efficiency, the task of pairing Portuguese addresses. The proposed solution fits in the Deep Learning field and has its main focus on Siamese Neural Networks of Pre-Trained Transformers. In this field, there are already promising results for similar tasks, which prove the viability of Deep Learning models for solving this kind of problem. The obtained results on a real address matching task proved that the proposed solution is a promising approach. The model is able to map the addresses in this dataset with an accuracy never lower than 94% on Artery level and 90% on Door level.