Existing research demonstrates that pre-decisional information sampling strategies are often stable within a given person while varying greatly across people. However, it remains largely unknown what drives these individual differences, that is, why in some circumstances we collect information more idiosyncratically. In this brief report, we present a pre-registered online study of spatial search. Using a novel technique that combines machine-learning dimension reduction and sequence alignment algorithms, we quantify the extent to which the shape and temporal properties of a search trajectory are idiosyncratic. We show that this metric increases (trajectories become more idiosyncratic) when a person is better informed about the likely location of the search target, while poorly informed individuals seem more likely to resort to default search routines determined bottom-up by the properties of the search field. This shows that when many people independently attempt to solve a task in a similar way, they are not necessarily "onto something."