How to Classify Documents whether they are similar to previous documents or not?
Only way to check which is best model is to try different in practise and compare. Do you have any documents to train with that are not similar? Do you have labels or topics assigned to these 100 ?

Assuming you have some irrelevant documents you can use a "spam" approach to this type of problem. 

I would begin with Bag of Words representing of your documents for a Naive Bayes model generation (common, good performing, baseline here) - this link http://sebastianraschka.com/Articles/2014_naive_bayes_1.html#naive-bayes-and-text-classification is a good explanation of this for documents .

I would also try using a library like Python gensim that will allow you to try its various methods, and you could try that library's Doc2Vec method. Then train a linear discriminant classifier like Log. Regression with the document vectors it gives you, and try see if its resulting accuracy is better than the Naive Bayes method.