Deep convolutional neural networks (CNN) follow roughly the architecture of biological visual systems and have shown a performance comparable to human observers in object classification tasks. In this study, three deep neural networks pretrained for image classification were tested in visual search for simple features and for feature configurations. The results reveal a qualitative difference from human performance. It appears that there is no clear difference between searches for simple features that pop out in experiments with humans and for feature configurations that exhibit strict capacity limitations in human vision. Both types of stimuli reveal comparable capacity limitations in the neural networks tested here.