We tested a parallel neural network model of visual search, and found that it located targets more quickly when allowed to take several fast guesses. We suggest that this serially iterated parallel search may be the mode used by the visual system, in accord with theories such as the Guided Search model. Furthermore, in our model the most efficient mode of processing varied with the type of search. If the nature of visual search varies with task demands, seemingly contradictory findings can be reconciled.