Neural Architecture Search (NAS) is a technique for automating deep learning model design, but its high computational cost remains a significant challenge. This paper introduces the activation diversity function score (ADFS) as a zero-shot proxy for evaluating candidate convolutional neural architectures without requiring full training on histopathological datasets, namely colorectal polyps and cervical cells. ADFS measures activation diversity across layers, weights, and updates, favoring architectures that learn rich, non-redundant feature representations. We apply ADFS to a stochastic multi-layer perceptron generation process, varying the number of layers, neurons per layer, activation functions, and skip connections. Architectures are ranked based on ADFS and undergo constrained training to validate performance. Experiments on structured medical histopathological datasets show that ADFS-selected models achieve high accuracy with significantly reduced training costs, aligning with Green AI initiatives. This work demonstrates the feasibility of efficient zero-shot NAS for deep learning. This makes model selection scalable and accessible.