Embedding distribution alignment is an approach to transfer knowledge from label-abundant electro-optical (EO) images to the label-scarce synthetic aperture radar (SAR) modality. However, this approach assumes that it is possible to learn a useful and discriminative EO representation via a neural network. In this work, we study the properties of such a representation. We analyze a recent result showing that supervised contrastive learning can improve transfer performance and find that its reduction of the effective dimension of the embedding is crucial to successful transfer. We then show that directly optimizing for this property can yield even better down-stream accuracy. Finally, we show that the powerful representation of an EO foundation model is insufficient for alignment due to its generality, but that additional representation learning can recover alignment performance.