This paper describes RMIT-IR team’s participation in the EXIST Lab at CLEF 2024. The proposed approaches aim to address sexism characterization on microblog posts (Tasks 1, 2, and 3) and sexism identification on memes (Task 4). For Tasks 1-3, we studied the effectiveness of zero-shot In-Context Learning (ICL) with off-the-shelf pre-trained Large Language Models (LLMs) to mimic the scenario of minimal intervention of a practitioner aiming to build sexism characterization systems. Our approaches for meme classification (Task 4) utilize CLIP (Contrastive Language-Image Pre-training) to experiment with multi-modal embeddings and zero-shot sexism identification models. We report the performance of our approaches under the learning with disagreements regime (Soft evaluation) and also for label predictions (Hard evaluation). The code of our submission is available at https://github.com/rmit-ir/exist2024/.