Pairing a neuro-symbolic model with library learning to facilitate program induction seems a promising way of fostering open-ended innovation, by leveraging the robustness, expressivity, and extrapolative capabilities of programs. This paper investigates how Open-Ended Dreamer (OED), an unsupervised diversity-oriented neuro-symbolic learner built upon DreamCoder (Ellis et al., 2021), may support open-ended program discovery. By alternating between phases of generation, selection, and abstraction, OED aims to expand a hierarchical library of diversity-enabling building blocks (in the form of programs), which are subsequently reused and composed in later iterations. As a first test-bed, we apply OED to a tower building domain and investigate the impact of library learning, neural guidance, innate priors, and language or environmental pressures on the formation of symbolic knowledge. Our experiments suggest that promoting greater exploration and stochasticity is crucial to offset the bias introduced by the growing language, and foster more creative divergence.