Using the whole dataset for testing (not validation) in case of small datasets
Considering that you have not used a cross-validation strategy, you could try to use the LOOCV (Leave One Out Cross Validation) strategy, so you have several splits (as many as samples considered in your training dataset) and still leave some samples as a final never-seen-before validation set.
This is costly from a performance perspective, but interesting for small datasets as in your case. Another option, in case the dataset is not really small, is usin k-fold cross-validation (with k being 5, 10...).
A post with practical info can be found here and a related topic discussion can be found here