Bioluminescence tomography (BLT) is a powerful molecular imaging technology designed for the localization and quantification of bioluminescent sources in vivo. With the forward process modeled by the diffusion approximation equation, BLT is the inverse problem to reconstruct the distribution of internal bioluminescent sources subject to Cauchy data. Due to the non-uniqueness of BLT in general, adequate prior information such as nonnegativity and source support constraints must be utilized to obtain a physically favorable BLT solution. Iterative algorithms such as the well-known Expectation Maximization (EM) algorithm and the Landweber algorithm which are suitable for incorporating these knowledge-based constraints are widely used in practice. In the current work, we investigate the application of a superiorization-like approach to BLT. A superiorization-like version of prototypical iterative algorithms for BLT in a general framework, denoted by S-BLT, is presented. For the EM algorithm as the underlying iterative algorithms for BLT and S-BLT, superiorized by the total variation (TV) merit function, preliminary simulation results for a heterogeneous phantom are reported to demonstrate the viability of the approach and evaluate the performance of the proposed algorithm. It is found that total variation superiorization of BLT can significantly improve the visualization effect of the reconstruction with the sources set as a particular case of radial basis functions.