Overall, our direct classification strategy showed a better performance in identifying the acute fish toxicity categories of the chemicals directly from the molecular descriptors, rather than passing via a QSAR regression model. We also observed a higher level of consistency between the categories generated by our models compared to another prediction method (i.e. Aalizadeh model). We interpret that the main reason behind the overall better performance of the direct classification approaches is first and foremost the fact the uncertainties associated with the QSAR regression models do not impact the categorization. Additionally, the inclusion of a larger number of descriptors in such models implies that higher levels of structural features are incorporated. In fact, the low level of variance explained by individual variables further confirms this hypothesis. Our direct classification model can be easily adapted to different types of pre-defined (acute fish toxicity) categories, as demonstrated here by classifying the chemicals following the categories for short-term (acute) aquatic hazard of the GHS. Overall, these results indicate the viability of the classification strategy as a means of chemical prioritization and management.