The adverse outcome pathway (AOP) paradigm has brought mechanism of action in the spotlight of regulatory toxicology, linking biochemical interactions on cellular level (i.e. molecular initiating event, MIE) via key events to adverse outcomes (AOs) on population level. Developments on mechanistic understanding of endocrine disruption (ED) has brought forward MIEs associated with early neurode-velopmental interference [1] and metabolic disruption [2], describing agonistic and antagonistic interactions with receptors such as constitutive androstane receptor (CAR), estrogen receptor alpha (ERα), farsenoid X receptor (FXR), and glucocorticoid receptor (GR). High confidence on in silico predictions is dictated by high quality training data on mechanistically relevant endpoints, where well-defined chemistry is covered. Based on Tox21 in vitro assays describing events of agonism and antagonism for 13 receptors linked to ED, 23 in silico models were developed using Random Forest Classification. To quantify measures of uncertainty per prediction a Conformal Prediction framework was employed. In order to assess whether currently available models can confidently predict endocrine disrupting chemicals (EDCs), screening of EURION reference chemicals was conducted. The EURION cluster is a constellation of 8 research consortia aiming to improve endocrine disruption identification. Preliminary results revealed strengths in the use of in silico models for screening of current ED chemical landscape, and data gaps that need to be considered for next steps.