There are two prevailing methods for pre-training on large datasets to learn transferable representations: 1) supervised pre-training on large but weakly-labeled datasets; 2) contrastively training on image only and image, text pairs. While supervised pre-training learns good representations that can be transferred to a wide range of tasks, contrastively models such as CLIP have demonstrated unprecedented zero-shot transfer. In this work, we compare the transferability of the two aforementioned methods to multiple downstream tasks. The pre-training distributions we consider include YFCC, Conceptual Captions, and ImageNet-21K while pre-training objectives range from supervised to SimCLR, CLIP, and SLIP. We observe that different pre-training methods with the same training source transfer similarly given their ImageNet accuracy.