Proteins within a family sharing sequence and structure similarity due to a common evolutionary origin often also share functional similarities. Clustering of proteins therefore offers valuable insights, enabling the transfer of features and annotations from well-studied proteins to less-investigated ones. On a local scale, clustering helps identify patterns within specific protein families. On a larger scale, it provides insights into the entire protein universe, showcasing relationships that may not be immediately apparent. Traditionally, this was done at the sequence level or with the use of experimentally resolved protein structures, but the advent of deep learning in protein bioinformatics has brought new options to the table, increasing the breadth, depth, and diversity of similarity metrics and clustering approaches.