The results of our direct classification model showed its power in categorizing the chemicals in terms their acute fish toxicity based on their specific molecular descriptors. Our strategy can overcome the continuity assumption of QSAR models, which are conventionally used to fill experimental data gaps in chemical assessment of structurally similar compounds, directly impacting the size of the training set. In other words, with our direct classification approach the experimental datasets from different sources and for different chemical families can be grouped to generate larger training sets resulting in higher accuracy predictions. As demonstrated here with the direct classification of the chemicals in the Norman dataset into hazard categories defined by the GHS (based on acute fish toxicity), our approach can be adapted to different predefined categories as prescribed by various international regulations and/or classification or labeling systems. The direct classification approach can be expanded to other hazard categories (e.g. chronic toxicity) as well as to fate (e.g. mobility or persistence) and shows great potential for improving in-silico tools for chemical hazard and risk assessment.