Activity and property landscapes have been extensively studied, however, most of the studies are focused on small organic molecules. This fact could be explained by the lack of a proper fingerprint to represent peptides. However, bioinformatics approaches allow the identification of activity cliffs using uniquely the sequence alignment of peptides. Parallelly, in the last years, chemoinformatics approaches (e.g. molecular similarity metrics) based on topologies, connectivity, tridimensional features, and molecular properties offer a new alternative to study in depth more complex molecules , like peptides.