Image-with-text memes are a dominant form of online communication, and much of their spread happens through meme templates which are recurring visual formats that users adapt with new text or imagery. Most prior work scores memes individually for engagement or harmful content, an approach that is structurally blind to templates. Templates might amplify these patterns and enable coordinated harassment, which becomes visible only when memes are grouped by shared template. We formalize meme template identification as a semi-open-set recognition problem, requiring methods to both classify known templates and reject template-free memes and non-meme content. We introduce an evaluation framework spanning a controlled setting (1,704 ImgFlip templates) and a heterogeneous real-world social media sample, comparing supervised CNN- and distance-based methods, unsupervised density-based clustering, a novel fused SigLIP2+DINOv2 representation, and a retrieval-augmented LLM pipeline. In real-world social media sources dedicated to meme sharing, we find that only 21% of images use a known template, while 58% are template-free and 21% are not memes at all; identification performance also drops sharply from the controlled setting (best MCC 0.974 to 0.684). The loss comes mainly from deciding whether an image is a template-based meme at all, rather than identifying the template. Our dataset of 1.1M soft-labeled social media images, together with code for reproducibility, is available upon request.

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