Motivated by the inherent data scarcity in the medical domain, this work studies few-shot retinal disease classification’ using the Brazilian Multilabel Ophtalmological Dataset. We compare different network architectures and non-trivial data augmentations under the application of the Reptile Algorithm, conducting quantitative and qualitative analysis. Regarding the architectures, we observe that Swin outperforms ViT and ResNet. We also observe that clever data augmentations not only improve performance, but can also generate prediction confidence distributions that are more interpretable and trustworthy. Further-more, pre-training the models with domain-specific data leads to superior ability of the models to detect the relevant patterns in the images. Code is available at github.com/gabjp/few-shot-BRSET.