Detection of intrinsic or extrinsically administered chromophores and photo-absorbing nanoparticles has been achieved by multi-spectral optoacoustic tomography (MSOT). The detection sensitivity of MSOT depends not only on the signal to noise ratio considerations, as in conventional optoacoustic (photoacoustic) tomography implementations, but also on the ability to resolve the molecular targets of interest from the absorbing tissue background by means of spectral unmixing or sub-pixel detection methods. However, it is not known which unmixing methods are optimally suited for the characteristics of multispectral optoacoustic images. In this work we investigated the performance of different sub-pixel detection methods, typically used in remote sensing hyperspectral imaging, within the context of MSOT. A quantitative comparison of the different algorithmic approaches was carried out in an effort to identify methods that operate optimally under the particulars of molecular imaging applications. We find that statistical sub-pixel detection methods can demonstrate a unique detection performance with up to five times enhanced sensitivity as compared to linear unmixing approximations, under the condition that the optical agent of interest is sparsely present within the tissue volume, as common when using targeted agents and reporter genes.