In the last two decades, there has been significant advancement in heuristics for inducing Bayesian belief networks for the purpose of automatic distillation of knowledge from masses of data with target concepts. However, there are various circumstances where we are confronted to fix a set of most influencing variables in modelling of class variable. This arises in provision of confidence measures on set of variables used in the structure learning of data. In this study, we have tweaked empirical as well as theoretical aspects of various feature selection evaluators, their corresponding searching methods under six well known scoring functions in K2 which is a notable structure learning technique in Bayesian belief network. We have come up with some useful findings for overall computationally efficient approach among eleven evaluators. This analysis is useful in inducing better structure from the given dataset in imparting improved performance metric for Bayesian belief network.