This paper investigates the effectiveness of Explainable Artificial Intelligence (XAI) methods in making Vision-Language Models (VLMs), specifically CLIP, interpretable within art-historical contexts. It evaluates seven XAI methods through zero-shot localization experiments and human interpretability studies, concluding that their effectiveness depends on the conceptual stability and representational availability of the examined categories.