ECFP fingerprints are extensively adopted as the molecular descriptors for the machine-learning based QSAR or QSPR study (Ekins et al., 2010; Hu et al., 2012; Braga et al., 2015, 2017; Koutsoukas et al., 2016; Varsou et al., 2017; Wang et al., 2017; Yang et al., 2017). However, it has not been adopted to predict the bitterant in the existing literature. In most of the previous works about the classification, 1024bit-ECFP4 or 2048bit-ECFP6 are often chosen by default. In this work, 1024bit-ECFP4, 2048bit-ECFP4, 1024bit-ECFP6, and 2048bit-ECFP6 are systematically explored, since ECFP6 possesses more structural features than ECFP4, and 2,048 bits can accommodate more structural features than 1024bits to alleviate the bit collision. Thus in this section, the trained models without the prior feature selection are used for the comparison.