Machine learning (ML) models for screening endocrine-disrupting chems. (EDCs), such as TSH receptor (TSHR) agonists, are essential for sound management of chems.Previous models for screening TSHR agonists were built on imbalanced datasets and lacked applicability domain (AD) characterization essential for regulatory application.Herein, an updated TSHR agonist dataset was built, for which the ratio of active to inactive compounds greatly increased to 1:2.6, and chem. spaces of structure-activity landscapes (SALs) were enhanced.Resulting models based on 7 mol. representations and 4 ML algorithms were proven to outperform previous ones.Weighted similarity d. (ρs) and weighted inconsistency of activities (IA) were proposed to characterize the SALs, and a state-of-the-art AD characterization methodol. ADSAL{ρs, IA} was established.An optimal classifier developed with PubChem fingerprints and the random forest algorithm, coupled with ADSAL{ρs ≥ 0.15, IA ≤ 0.65}, exhibited good performance on the validation set with the area under the receiver operating characteristic curve being 0.984 and balanced accuracy being 0.941 and identified 90 TSHR agonist classes that could not be found previously.The classifier together with the ADSAL{ρs, IA} may serve as efficient tools for screening EDCs, and the AD characterization methodol. may be applied to other ML models.