Due to the potential of Large Language Models (LLMs) to disrupt the way people interact with information systems across numerous industries, we have investigated options to extend functionality in the context of astronomy science archives. A frequent request by the archive users is to have the ability to search for specific types of objects in archival data, but this information is typically not available given the difficulty of classifying the billions of objects present in astronomical catalogues. In an attempt to address this request by the users, we present a proof of concept implementation aiming to enhance searches in the ESA Astronomy Science Archives. This could be done extending ESASky AI capabilities through the interaction with LLMs and Retrieval Augmented Generation using the information contained in CDS’ SIMBAD astronomical object database. The proposed proof of concept already shows that our implementation could offer new capabilities for astronomers when leveraging ESASky for their research