Fiction is a challenging genre for automatic theme identification. Unlike other types of documents,such as physics academic papers, fiction does not always name the concepts it addresses, but rather implies them through subtle clues. Fiction also uses metaphors intentionally to convey deeper meanings. To make Swedish LGBTQ+ fiction more accessible, the Queerlit database (https://queerlit.dh.gu.se/) provides subject indexing by information professionals. They use the QLIT thesaurus (based on Homosaurus) for LGBTQ+ themes and Swedish Subject Headings (SAO – Svenska Ämnesord) for non-LGBTQ+ themes. The indexing is comprehensive and retrospective, assigning terms to previously published Swedish fiction. This work aims to determine to what degree and under which conditions is it possible to automatically assign subject index terms from QLIT, in order to estimate the usefulness ofautomatic tools to support subject indexing conducted by information professionals. This process may require a large number of training documents which are not available (the entire Queerlit database has about 2000 works indexed and QLIT has about 800 terms, while SAO is much bigger). Therefore, another approach will be explored – whether automatically extracted terms from the texts provide the potential to complement existing, professionally assigned terms from QLIT and SAO. We experiment with zero-shot classification transformers and topic modeling.The proposed paper will present the intermediate results of different methods applied to available texts from the QLIT database. It is important to note that the project is currently in an exploratory phase and that the presentation is intended to showcase how different approaches have both failed and succeeded. We also intend to highlight areas of possible applicability specifically from the perspective afforded by the QLIT thesaurus, i.e., the appropriateness of the methods for Swedish LGBTQ+ fiction. We will also discuss the challenges and limitations of automatic theme identification for fiction, especially for LGBTQ+ themes that are often implicit or nuanced.