In this work, it is the first time that the fully experimental bitterants/non-bitterants dataset, consensus voting based on the multiple machine-learning algorithms, and ECFP fingerprints are adopted to build the bitter/bitterless classification models. Through the exhaustive parameter exploration with the five-fold cross-validation, all the models are carefully scrutinized by the Y-randomization test to ensure their reliability, and subsequently nine consensus models are constructed based on the individual or average models, which differ in term of accuracy, speed and diversity of models. Evaluation on the three external test set from Wiener et al. demonstrates that e-Bitter outperforms bitterX on these three test sets; e-Bitter harvests the better results than BitterPredict for the “Bitter New” and “Phytochemical Dictionary” dataset; BitterPredict demonstrates slightly better performance than e-Bitter for the “UNIMI set” dataset. To automate the whole process, we develop a graphic e-Bitter program for the bitterant prediction or screening against the small-molecule database in batch. Additionally, e-Bitter program natively implements ECFP fingerprint, and more importantly, e-Bitter can vividly visualize the structural feature, feature importance, and feature partial derivative for any specific bit “1” in the ECFP fingerprint. We hope our work can provide a useful tool for the experimental scientist to rationally design and screen the bitterants.