This paper addresses the desktop search problem by considering varioustechniques for ranking results of a search query over thefile system. First, basic ranking techniques, which are based ona single file feature (e.g., file name, file content, access date, etc.)are considered. Next, two learning-based ranking schemes are presented, and are shown to be significantly more effective than the basic ranking methods. Finally, a novel ranking technique, based on query selectiveness is considered,for use during the cold-start period of the system. This method isalso shown to be empirically effective, even though it does notinvolve any learning.