If the validation set size is small, so as your training should be. Then you can perform cross-validation, even leave-one-out CV where the validation set is just one sample, assuming the training time is small. K-fold CV (including LOOCV) is typically more robust compared to using one constant validation set.
If you aren't able to do CV, performing searches over broad ranges may still be fine but you need to be cautious with finer resolutions since your results will probably have large variances. Eventually, it makes sense to do HPO but, in general, is not possible to fine tune your algorithm. Another option is to use heuristics for your hyper-parameters completely depending on your problem, if they exist.