Next-generation antivenoms comprised of recombinant monoclonal antitoxins are on the horizon. They carry substantial therapeutic potential for the development of safer and more effective treatment of the 2 million annual envenomations. However, such antivenoms are challenging to develop due to the high diversity and number of toxins that require neutralisation. To enable feasible biomanufacture, it is critical to keep the number of monoclonal antibodies in a therapeutic product low. This feat is only possible if one deconvolutes venom complexity and identifies groups of similar toxins that can potentially be neutralised by the same broadly-neutralising antibody. Therefore, in this project we clustered all currently published snake venom toxins from medically relevant toxin families using sequence-based clustering approaches. However, sequences might be insufficient predictors of cross-neutralisation potential as antibodies recognise structural, not sequence, features. As such we also investigated structural similarity by retrieving all available toxin 3D structures, as well as computationally predicting the over 1600 structures for all remaining toxins via a bioinformatic tool developed in house. This allowed us to identify clusters of toxins that share substantial sequence and/or structural similarity, possibly allowing for the prediction of which clusters can be neutralised by a single broadly-neutralising monoclonal antibody. In turn, such a prediction may enable a more targeted discovery strategy that can be employed to identify a minimum set of broadly-neutralising monoclonal antibodies that can cross-neutralise one or more whole venoms. We hope that this approach will act as a roadmap and subsequently lead to a significant acceleration of the discovery of broadly-neutralising antibodies against snake venom toxins.