The lack of access to large annotated datasets and legal concerns regarding patient privacy are limiting factors for many applications of deep learning in the retinal image analysis domain. Therefore the idea of generating synthetic retinal images, indiscernible from real data, has gained more interest. Generative adversarial networks (GANs) have proven to be a valuable framework for producing synthetic databases of anatomically consistent retinal fundus images. In Ophthalmology, GANs in particular have shown increased interest. We discuss here the potential advantages and limitations that need to be addressed before GANs can be widely adopted for retinal imaging.