Our application of AMUSET-TICA to alanine dipeptide, NTL9, and the FIP35 WW domain and our comparison of AMUSET-TICA's performance with AMUSEt, tICA and VAMPnets indicate that, for all three systems, AMUSET-TICA significantly outperforms both AMUSEt and tICA in accurately identifying the slowest CVs. Additionally, in the cases of NTL9 and the FIP35 WW domain, we find that AMUSET-TICA achieves comparable accuracy to the deep-learning based method, VAMPnets, but at a substantially lower computational cost.