The difficulty to write successful 19x19 go programs lies not only in the combinatorial complexity of go but also in the complexity of designing a good evaluation function containing a lot of knowledge. Leaving these obstacles aside, this paper defines very-little-knowledge evaluation functions used by programs playing on very small boards. The evaluation functions are based on two mathematical tools, distance and dimension, and not on domaindependent knowledge. After a qualitative assessment of each evaluation function, we built several programs playing on 4x4 boards by using tree search associated with these evaluation functions. We set up an experiment to select the best programs and identify the relevant features of these evaluation functions. Thanks to the results obtained by these very-little-knowledge-based programs, we can foresee the usefulness of each evaluation function.