The human visual system has been extensively trained to deal with objects and natural images, giving it the opportunity to develop robust strategies to quickly identify categories and exemplars. Although it is known that the memory capacity for images is massive (Standing, 1973), the fidelity with which human memory can represent such a large number of images is an outstanding question. We conducted three large-scale memory experiments to determine the details remembered per item, by systematically varying the amount of detail required to succeed in subsequent memory tests. Our results show that contrary to the commonly accepted view that long-term memory representations contain only the gist of what was seen, long-term memory can store thousands of items with a large amount of detail per item. Further, item analyses reveal that memory for an object or a natural image depends on the extent to which it is conceptually distinct from other items in the memory set, and not necessarily on the featural distinctiveness along shape or color dimensions. These findings suggest a ?conceptual hook? is necessary for maintaining a large number of high-fidelity representations. Altogether, the results present a great challenge to models of object and natural scene recognition, which must be able to account for such a large and detailed storage capacity.