Supervised learning approaches have proven to be useful in diagnosing Osteoarthritis from X-ray images, aiding professionals in an otherwise time-consuming and subjective process. However, in the medical field, labeled data is scarce. For this reason, we investigate a contrastive self-supervised approach, SimCLR, capable of learning useful representations from unlabeled data. Specifically, we explore a core component of this method – the data augmentation techniques. While these augmentations are highly effective in introducing variability in conventional image datasets, they are too aggressive for medical images, often altering their semantic meaning. In this paper, we implement custom anatomy-aware augmentation techniques, which aim to preserve the main region of interest needed for a diagnosis. We evaluate these anatomy-aware augmentations including Gaussian blur, Contrast enhancement, Random resized crop, and Random erasing, against their classical counterparts by training multiple encoders based on different combinations of those augmentations. The findings of our study have shown that utilizing this anatomy-aware approach for all data augmentations a model uses does not lead to a significant improvement in its performance. However, selective use of anatomy-awareness on geometric-based approaches seems to show promising initial results.