In silico models are essential for the development of integrated alternative methods to identify organ level toxicity and lead toward the replacement of animal testing. These models include (quantitative) structure-activity relationships ((Q)SARs) and, importantly, the identification of structural alerts associated with defined toxicological end points. Structural alerts are able both to predict toxicity directly and assist in the formation of categories to facilitate read-across. They are particularly important to decipher the myriad mechanisms of action that result in organ level toxicity. The aim of this study was to develop novel structural alerts for nuclear receptor (NR) ligands that are associated with inducing hepatic steatosis and to show the vast number of existing data that are available. Current knowledge on NR agonists was extended with data from the ChEMBL database (12,713 chemicals in total) of bioactive molecules and from studying NR ligand-binding interactions within the protein database (PDB, 624 human NR structure files). A computational structural alert based workflow was developed using KNIME from these data using molecular fragments and other relevant chemical features. In total, 214 structural features were recorded computationally as SMARTS strings, and therefore, they can be used for grouping and screening during drug development and hazard assessment and provide knowledge to anchor adverse outcome pathways (AOPs) via their molecular initiating events (MIEs).