The blog post explores the use of bloom filters in creating a space-efficient full text search index for documents. It starts with a basic implementation from 2013, which involves creating a bloom filter for each document containing all its words. The drawback is that querying becomes linear with the number of documents, making it impractical for large datasets. The author proposes scaling this method to larger document corpuses but identifies significant challenges, particularly regarding query performance. The key advantage of bloom filters is their compact size compared to traditional inverted indexes. For small websites, this means an entire search index can be as small as an image file. However, as the corpus size increases, the overlap between documents complicates matters. The author tests ideas like sorting filters and structuring them into trees, but these approaches fail due to the high dimensionality of language, where words often appear in multiple contexts. The concept of an inverted index of bloom filters emerges as a potential solution. This method uses a tree structure based on the dictionary, where each leaf contains pointers to the bloom filters of documents that include specific words. While this approach improves space efficiency and achieves logarithmic query time complexity, it still faces limitations. The author calculates that for a typical English dictionary and document size, bloom filters become less efficient for more than 7,200 documents. This inefficiency arises because each document's bloom filter encodes every word anew, unlike an inverted index, which shares word entries among documents. The analysis emphasizes that while bloom filters can outperform traditional methods in specific scenarios, their advantages diminish rapidly with larger datasets.