When I analyze somatic mutations, I usually judge them according to many annotation filters such as conserved genomic regions, segmental_duplications and integrated databases (cosmic). Especially, I didn't believe any filter-based annotation in annovar which are SIFT or mutation_assessor because some cases have lower score but they are likely to be relevant to the cancer-disease or have dominant effects for disease.

From this points, I am wondering what useful annotation filter you use for mutation in exome seq?

Does any definite and reliable annotation filter exist?

It is always hard for bioinformaticians to differentiate artifacts or false_positives from real variants.
I do not believe that there is a definite and reliable filter but I do like [KGGSeq][1] very much because:

 - it combines several prediction programs into 1 variable
 - it uses many different databases
 - you can add your own databases

So strategy depends on the data you have, we usually have trio's and look for denovo variants, so:

1. only keep denovo: WT in parents and HET in affected
2. remove all that are present in ExAc database
3. remove synonymous, non-frameshifts and non-coding
4. prioritize based on the prediction programs score (so no filtering here)

 [1]: http://statgenpro.psychiatry.hku.hk/limx/kggseq/