Moreover, (eco)toxicity evaluations recommend circumventing the assignment of the observed toxicity to chemicals in polluted and treated waters because only a minor fraction of the observed toxicity can be allocated to individual compounds in mixtures of pollutants and their transformation products. Chemical reaction networks generated through high-throughput computational chemistry have great potential to address this knowledge gaps by providing atomistic evidence on transient chemical species and overlooked products. For example, intermediates reacting at diffusion controlled rates in ozonation processes typically require concentrations exceeding approximately 10 -12 M to be relevant during water treatment operations. 2 Such cut-off criteria would support the systematic search of compounds unaccounted so far in microkinetic modeling exercises. Moreover, the numerous compounds acquired in CRN databases might prove highly beneficial for prediction of high resolution mass spectra with deep learning-based approaches. Finally, predictions of potentially harmful or toxic compounds or compound (sub)structures from CRN explorations of oxidative water treatment could become pivotal for the evaluation of environmental impacts of chemicals in early design phases.