Do word embeddings help with out of vocab tokens?
By definition an out of vocabulary word (OOV) is a word which haven't been seen in the training data, so it's virtually impossible to know which other word it is similar to since this would have to be determined with some training data. In the example that you mention (synonym), what you could use is a resource such as WordNet which tells you which word it's similar to, then you can use the other word embedding.

Cases of misspelled words is a different story: you could pass your OOV words through string matching techniques, or maybe use a character-based NN which would recognize the word despite the misspelling.

Both cases could be part of a pre-processing stage.