The study of literary metaphors plays an integral part in literature-focused disciplines within the humanities. In the field of Natural Language Processing (NLP), computational metaphor detection (MD) has produced a wealth of approaches focusing on everyday metaphors (Ptiček and Dobša 2023). Computational literary MD, however, has received considerably less attention. In their Graph Project, Kesarwani et al. (2017) have applied rule-based and statistical machine learning approaches to an English poetry corpus. The aim of this paper is to take the field of literary MD one step further by using the NLP state-of-the-art approach of fine-tuning Large Language Models (LLMs) on the Graph project’s datasets.<ref xmlns="http://www.tei-c.org/ns/1.0" n="1" target="ftn1" /> Considering the small size of the relevant datasets, the few-shot learning approach SetFit is used alongside the more established fine-tuning Transformers approach to compare their performance.