The entropic associative memory (EAM) is a computational model of natural memory incorporating some of its putative properties of being associative, distributed, declarative, abstractive, and constructive. Previous experiments satisfactorily tested the model on structured, homogeneous, and conventional data: images of manuscript digits and letters, images of clothing, and phone representations. In this work, we show that EAM appropriately stores, recognizes, and retrieves diverse and complex images of animals and vehicles in the CIFAR-10 dataset. The memory system generates meaningful retrieval association chains for such complex images. The retrieved objects can be seen as proper memories, associated recollections, or products of imagination, but also as noise. Furthermore, EAM is able to reject patched cues not recognizable by people, providing additional support for the similarity between EAM and natural memory; in contrast, autoencoders reproduce the images with the patch, and other memory models retrieve the original input cues completely.