Ionic liquids (ILs) possess a unique physicochemical profile providing a wide range of applications. Their almost limitless structural possibilities allow the design of task specific ILs. However, their "greenness", specifically their claimed relative non toxicity has been frequently questioned, hindering their REACH registration processes and so, their final application. Since the vast majority of ILs has yet to be synthesized, the development of chemoinformatics tools efficiently profiling their hazardous potential becomes essential. In this work we introduce a reliable, predictive, simple and chemically interpretable classification and regression tree (CART) classifier enabling the prioritization of ILs with a favourable cytotoxicity profile. Besides a good predictive capability (81% / 75% / 83% of accuracy / sensitivity / specificity in an external evaluation set), the other salient feature of the proposed cytotoxicity CART classifier is their simplicity and transparent chemical interpretation based on structural molecular fragments. The essentials of the current structure-cytotoxicity relationships of ILs are faithfully reproduced by this model, supporting its biophysical relevance and the reliability of the resultant predictions. By inspecting the structure of the CART several moieties that can be regarded as "cytotoxicophores" were identified and used to establish a set of SAR trends specifically aimed to prioritise low cytotoxicity ILs. Finally, we demonstrated the suitability of the joint use of the CART classifier and a group fusion similarity search as a virtual screening strategy for the automatic prioritisation of safe ILs disperse in a data set of ILs of moderate to very high cytotoxicity.