The common product between two multisets or functions can be understood as being analogue to the inner product in real vector or function spaces in spite of its non-linear nature. In addition to other interesting features, it also allows respective correlations to be derived which, in addition to their conceptual and computational simplicity, have been verified to be able to provide enhanced results in tasks such as template matching. The multiset-based correlations based on the real-valued multiset Jaccard and coincidence indices are compared in this work, with encouraging results which have immediate implications not only in pattern recognition and deep learning, but also in scientific modeling in general. As expected, the multiset correlation methods, and especially the coincidence index, presented remarkable performance characterized by sharper and narrower peaks while secondary peaks were attenuated, even in presence of noise. In particular, the two methods derived from the coincidence index led to the sharpest and narrowest peaks, as well as intense attenuation of the secondary peaks. The cross correlation, however, presented the best robustness to symmetric additive noise, which suggested a combination of the considered approaches. After a preliminary investigation of the performance of the multiset approaches, as well as the classic cross-correlation, a systematic comparison framework is proposed and applied for the study of the aforementioned methods. Several interesting results are reported, including the confirmation, at least for the considered type of data, of the coincidence correlation as providing enhanced performance regarding detection of narrow, sharp peaks while secondary matches are duly attenuated. The combined method also confirmed its good performance for signals in presence of intense additive noise.