Investigations into sensory coding in the visual system have typically relied on the use of either simple, unnatural visual stimuli or natural images. Simple stimuli, such as Gabor patches, have been effective when looking at single neurons in early visual areas such as V1 but seldom produce large responses from mid-level visual neurons or neural populations with diverse tuning. Many types of "naturalistic" image models have been developed recently, which bridge the gap between overly simple stimuli and experimentally infeasible natural images. These stimuli can vary along a large number of feature dimensions, introducing new challenges when trying to map those features to neural activity. This "curse of dimensionality" is exacerbated when neural responses are themselves high dimensional, such as when recording neural populations with implanted multielectrode arrays. We propose a method that searches high-dimensional stimulus spaces for characterizing neural population manifolds in a closed-loop experimental design. Stimuli were generated using a deep neural network in each block by using neural responses to previous stimuli to make predictions about the relationship between the latent space of the image model and neural responses. We found that these latent variables from the deep generative image model explained stronger linear relationships with neural activity than various alternative forms of image compression. This result reinforces the potential for deep generative image models for efficient characterization of high-dimensional tuning manifolds for visual neural populations.