Near-native protein structure prediction through Template Based Modelling (TBM) has been a major realistic goal of structural biology for several years. The TBM algorithms require the best-set of templates for a target protein sequence to maximally cover it and construct its correct topology. However, the accuracy of such prediction algorithms suffers from the algorithmic and logical problems of our template search measures which fail to quickly screen reliable structures for a target sequence. In this study, we employ the culled PDB95 dataset of 41,967 templates to predict the CASP10 target T0752 models for assessing the efficiency of the usually employ search engines PSI-BLAST and HHPred. Our analysis presents a detailed study in order to open new vistas for improving the accuracy of TBM prediction methodologies. It reveals weaknesses of most popular template search measures and thereby briefly provides a significant insight into the qualities of a foreseen template search algorithm to illustrate the need for a more reliable template search algorithm.