Aim of this work is to develop a comparative analysis to evaluate the performances of several Bayesian regression approaches in the high-dimensional context where the number of observations is very small with respect to the number of predictors. Moreover in this study we assume that the predictors can be expressed only as binary variables coding the presence or the absence of a particular charac- teristic of the system. This binary structure is very present in many real studies, in particular in laboratory experimentation.