  Recent results from ATLAS gives a Higgs mass of 125.5 GeV, further constrain
already highly constrained supersymmetric models such as pMSSM or CMSSM/mSUGRA.
Finding potentially discoverable and non-excluded regions of model parameter
space is becoming increasingly difficult. Several groups have invested large
effort in studying the consequences of Higgs mass bounds, upper limits on rare
$B$-meson decays, and limits on relic dark matter density on constrained
models, aiming at predicting superpartner masses, and establishing likelihood
of SUSY models compared to that of the Standard Model vis-\'a-vis experimental
data. In this paper a framework for efficient search for discoverable,
non-excluded regions of different SUSY spaces giving specific experimental
signature of interest is presented. The method employs an improved Markov Chain
Monte Carlo (MCMC) scheme exploiting an iteratively updated likelihood function
to guide search for viable models. Existing experimental and theoretical bounds
as well as the LHC discovery potential are taken into account. This includes
recent bounds on relic dark matter density, the Higgs sector and rare
$B$-mesons decays. A clustering algorithm is applied to classify selected
models according to expected phenomenology enabling automated choice of
experimental benchmarks and regions to be used for optimizing searches. The aim
is to provide experimentalist with a viable tool helping to target experimental
signatures to search for, once a class of models of interest is established. As
an example a search for viable CMSSM models with $\tau$-lepton signatures
observable with the 2012 LHC data set is presented. In the search 105209 unique
models were probed. From these, ten reference benchmark points covering
different ranges of phenomenological observables at the LHC were selected.
