This paper introduces an approach developed for multimodal meme analysis, specifically targeting the identification of persuasion techniques embedded within memes. Our methodology integrates Large Language Models (LLMs) and contrastive learning image encoders to discern the presence of persuasive elements in memes across diverse platforms. By capitalizing on the contextual understanding facilitated by LLMs and the discriminative power of contrastive learning for image encoding, our framework provides a robust solution for detecting and classifying memes with persuasion techniques. The system was used in Task 4 of Semeval 2024, precisely for Substask 2b (binary classification of presence of persuasion techniques). It showed promising results overall, achieving a Macro-F1=0.7986 on the English test data (i.e., the language the system was trained on) and Macro-F1=0.66777/0.47917/0.5554, respectively, on the other three “surprise” languages proposed by the task organizers, i.e., Bulgarian, North Macedonian and Arabic. The paper provides an overview of the system, along with a discussion of the results obtained and its main limitations.