Cnidaria is a rich phylum that includes thousands of marine species. In this study, we focused on Anthozoa and Hydrozoa that are represented by the Nematostella vectensis (Sea anemone) and Hydra magnipapillata genomes. We present a method for ranking the toxin-like candidates from complete proteomes of Cnidaria. Toxin-like functions were revealed using ClanTox, a statistical machine-learning predictor trained on ion channel inhibitors from venomous animals. Fundamental features that were emphasized in training ClanTox include cysteines and their spacing along the sequences. Among the 83,000 proteins derived from Cnidaria representatives, we found 170 candidates that fulfill the properties of toxin-like-proteins, the vast majority of which were previously unrecognized as toxins. An additional 394 short proteins exhibit characteristics of toxin-like proteins at a moderate degree of confidence. Remarkably, only 11% of the predicted toxin-like proteins were previously classified as toxins. Based on our prediction methodology and manual annotation, we inferred functions for over 400 of these proteins. Such functions include protease inhibitors, membrane pore formation, ion channel blockers and metal binding proteins. Many of the proteins belong to small families of paralogs. We conclude that the evolutionary expansion of toxin-like proteins in Cnidaria contributes to their fitness in the complex environment of the aquatic ecosystem.