Consensus strategies have been widely applied in many different scientific fields, based on the assumption that the fusion of several sources of information increases the outcome reliability. Despite the widespread application of consensus approaches, their advantages in quantitative structure-activity relationships (QSAR) modeling have not been thoroughly evaluated, mainly due to the lack of appropriate large-scale datasets. In this study, we evaluated the advantages and drawbacks of consensus approaches compared to single classification QSAR models. To this end, we used a dataset of three properties (androgen receptor binding, agonism and antagonism) for approximately 4,000 molecules with predictions performed by more than 20 QSAR models, made available in a large-scale collaborative project. The individual QSAR models were compared with two consensus approaches: majority voting and Bayes with discrete probability distributions, in both protective and non-protective form. Consensus strategies proved to be more accurate and to better cover the analyzed chemical space than individual QSARs on average, thus motivating their widespread application for property prediction. Scripts and data to reproduce results of this study are available for download.