In this study, we developed a random forest-based direct classification model to convert the molecular descriptors of chemicals to predefined acute fish toxicity categories. This model was developed, validated, and tested via an experimentally defined dataset of 96h LC50 values for acute fix toxicity for 907 organic chemicals. The result of this strategy was directly compared to the conventional two-step approach-first QSAR-based property prediction and then toxicity categorization-both for the acute fish toxicity data and a dataset of ≈ 23000 chemicals from Norman SusDat.