The combinatorial search problem arising in feature selection in high dimensional spaces is considered. Recently developed techniques based on the classical sequential methods and the (l; r) search called Floating search algorithms are compared against the Genetic approach to feature subset search. Both approaches have been designed with the view to give a good compromise between efficiency and effectiveness for large problems. The purpose of this paper is to investigate the applicability of these techniques to high dimensional problems of Feature Selection. The aim is to establish whether the properties inferred for these techniques from medium scale experiments involving up to a few tens of dimensions extend to dimensionalities of one order of magnitude higher. Further, relative merits of these techniques vis-a-vis such high dimensional problems are explored and the possibility of exploiting the best aspects of these methods to create a composite feature selection procedure with superior properties is considered. 1.