Self-supervised representation learning in computer vision heavily relies on hand-crafted image transformations to derive meaningful, invariant features. Yet, the literature has limited explorations on the impact of transformation design. This work delves into this relationship, particularly its effect on domains beyond natural images. We posit that transformation design acts as beneficial supervision. We establish that transformations influence representation features and clustering relevance, and further probe transformation design's effect on microscopy images, where class differences are subtler than in natural images, leading to more pronounced impacts on encoded features. Conclusively, we showcase that careful transformation selection, based on desired features, enhances performance by refining the resulting representation.