Bayesian optimization (BO) is a black-box search method particularly valued for its sample efficiency. It is especially effective when evaluations are very costly, such as in hyperparameter optimization or Neural Architecture Search (NAS). In this work, we design a fast NAS method based on BO. While Gaussian Processes underpin most BO approaches, we instead use deep ensembles. This allows us to construct a unified and improved representation, leveraging pretraining metrics and multiple evaluation fidelities, to accelerate the search. More specifically, we use a simultaneous pretraining scheme where multiple metrics are estimated concurrently. Consequently, a more general representation is obtained. A novel multi-fidelity approach is proposed, where the unified representation is improved both by high and low quality evaluations. These additions significantly accelerate the search time, finding the optimum on NAS-Bench-201 in an equivalent time and cost to performing as few as 50 to 80 evaluations. The accelerated search time translates to reduced costs, in terms of computing resources and energy consumption. As a result, applying this NAS method to real-world use cases becomes more practical and not prohibitively expensive. We demonstrate the effectiveness and generality of our approach on a custom search space. Based on the MOAT architecture, we design a search space of CNN-ViT hybrid networks. The search method yields a better-performing architecture than the baseline in only 70 evaluations.