Classifier subset selection (CSS) from a large ensemble is an effective way to design multiple classifier systems (MCSs). Given a validation dataset and a selection criterion, the task of CSS is reduced to searching the space of classifier subsets to find the optimal subset. This study investigates the search efficiency of genetic algorithm (GA) and sequential search methods for CSS. In experiments of handwritten digit recognition, we select a subset from 32 candidate classifiers with aim to achieve high accuracy of combination. The results show that in respect of optimality, no method wins others in all cases. All the methods are very fast except the generalized plus l and take away r(GPTA) method.