Nano-drones are capable of performing a vast amount of tasks that are not doable in any other, comparably versatile, way, including indoor surveillance, search and rescue, and inspection. Their reduced size and cost, as well as their limitations in terms of power for computational purposes (under 100 mW) make running deep learning models on these devices particularly challenging. The aim of this work is to study automated ways to design and deploy sufficiently tiny Neural Network (NN) architectures, reducing the number of parameters and operations of an input seed network. In order to address this task we employed a novel Network Architecture Search (NAS) technique called, Pruning In Time (PIT).